Measuring gender when you don’t have a gender measure: constructing a gender index using survey data

Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Smith, Peter M. [verfasserIn] Koehoorn, Mieke

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Gender Sex Labour force Gender roles Measurement Survey data

Anmerkung:	© The Author(s). 2016

Übergeordnetes Werk:	Enthalten in: International journal for equity in health - London : BioMed Central, 2002, 15(2016), 1 vom: 28. Mai
Übergeordnetes Werk:	volume:15 ; year:2016 ; number:1 ; day:28 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s12939-016-0370-4

Katalog-ID:	SPR028710649

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spelling	10.1186/s12939-016-0370-4 doi (DE-627)SPR028710649 (SPR)s12939-016-0370-4-e DE-627 ger DE-627 rakwb eng Smith, Peter M. verfasserin aut Measuring gender when you don’t have a gender measure: constructing a gender index using survey data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. Gender (dpeaa)DE-He213 Sex (dpeaa)DE-He213 Labour force (dpeaa)DE-He213 Gender roles (dpeaa)DE-He213 Measurement (dpeaa)DE-He213 Survey data (dpeaa)DE-He213 Koehoorn, Mieke aut Enthalten in International journal for equity in health London : BioMed Central, 2002 15(2016), 1 vom: 28. Mai (DE-627)356253716 (DE-600)2092056-8 1475-9276 nnns volume:15 year:2016 number:1 day:28 month:05 https://dx.doi.org/10.1186/s12939-016-0370-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2016 1 28 05
allfields_unstemmed	10.1186/s12939-016-0370-4 doi (DE-627)SPR028710649 (SPR)s12939-016-0370-4-e DE-627 ger DE-627 rakwb eng Smith, Peter M. verfasserin aut Measuring gender when you don’t have a gender measure: constructing a gender index using survey data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. Gender (dpeaa)DE-He213 Sex (dpeaa)DE-He213 Labour force (dpeaa)DE-He213 Gender roles (dpeaa)DE-He213 Measurement (dpeaa)DE-He213 Survey data (dpeaa)DE-He213 Koehoorn, Mieke aut Enthalten in International journal for equity in health London : BioMed Central, 2002 15(2016), 1 vom: 28. Mai (DE-627)356253716 (DE-600)2092056-8 1475-9276 nnns volume:15 year:2016 number:1 day:28 month:05 https://dx.doi.org/10.1186/s12939-016-0370-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2016 1 28 05
allfieldsGer	10.1186/s12939-016-0370-4 doi (DE-627)SPR028710649 (SPR)s12939-016-0370-4-e DE-627 ger DE-627 rakwb eng Smith, Peter M. verfasserin aut Measuring gender when you don’t have a gender measure: constructing a gender index using survey data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. Gender (dpeaa)DE-He213 Sex (dpeaa)DE-He213 Labour force (dpeaa)DE-He213 Gender roles (dpeaa)DE-He213 Measurement (dpeaa)DE-He213 Survey data (dpeaa)DE-He213 Koehoorn, Mieke aut Enthalten in International journal for equity in health London : BioMed Central, 2002 15(2016), 1 vom: 28. Mai (DE-627)356253716 (DE-600)2092056-8 1475-9276 nnns volume:15 year:2016 number:1 day:28 month:05 https://dx.doi.org/10.1186/s12939-016-0370-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2016 1 28 05
allfieldsSound	10.1186/s12939-016-0370-4 doi (DE-627)SPR028710649 (SPR)s12939-016-0370-4-e DE-627 ger DE-627 rakwb eng Smith, Peter M. verfasserin aut Measuring gender when you don’t have a gender measure: constructing a gender index using survey data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. Gender (dpeaa)DE-He213 Sex (dpeaa)DE-He213 Labour force (dpeaa)DE-He213 Gender roles (dpeaa)DE-He213 Measurement (dpeaa)DE-He213 Survey data (dpeaa)DE-He213 Koehoorn, Mieke aut Enthalten in International journal for equity in health London : BioMed Central, 2002 15(2016), 1 vom: 28. Mai (DE-627)356253716 (DE-600)2092056-8 1475-9276 nnns volume:15 year:2016 number:1 day:28 month:05 https://dx.doi.org/10.1186/s12939-016-0370-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_375 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2016 1 28 05
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author	Smith, Peter M.
spellingShingle	Smith, Peter M. misc Gender misc Sex misc Labour force misc Gender roles misc Measurement misc Survey data Measuring gender when you don’t have a gender measure: constructing a gender index using survey data
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topic_title	Measuring gender when you don’t have a gender measure: constructing a gender index using survey data Gender (dpeaa)DE-He213 Sex (dpeaa)DE-He213 Labour force (dpeaa)DE-He213 Gender roles (dpeaa)DE-He213 Measurement (dpeaa)DE-He213 Survey data (dpeaa)DE-He213
topic	misc Gender misc Sex misc Labour force misc Gender roles misc Measurement misc Survey data
topic_unstemmed	misc Gender misc Sex misc Labour force misc Gender roles misc Measurement misc Survey data
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title	Measuring gender when you don’t have a gender measure: constructing a gender index using survey data
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title_full	Measuring gender when you don’t have a gender measure: constructing a gender index using survey data
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title_sort	measuring gender when you don’t have a gender measure: constructing a gender index using survey data
title_auth	Measuring gender when you don’t have a gender measure: constructing a gender index using survey data
abstract	Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. © The Author(s). 2016
abstractGer	Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. © The Author(s). 2016
abstract_unstemmed	Background Disentangling the impacts of sex and gender in understanding male and female differences is increasingly recognised as an important aspect for advancing research and addressing knowledge gaps in the field of work-health. However, achieving this goal in secondary data analyses where direct measures of gender have not been collected is challenging. This study outlines the development of a gender index, focused on gender roles and institutionalised gender, using secondary survey data from the Canadian Labour Force survey. Using this index we then examined the distribution of gender index scores among men and women, and changes in gender roles among male and female labour force participants between 1997 and 2014. Methods We created our Labour Force Gender Index (LFGI) using information in four areas: responsibility for caring for children; occupation segregation; hours of work; and level of education. LFGI scores ranged from 0 to 10, with higher scores indicating more feminine gender roles. We examined correlations between each component in our measure and our total LFGI score. Using multivariable linear regression we examined change in LFGI score for male and female labour force participants between 1997 and 2014. Results Although women had higher LFGI scores, indicating greater feminine gender roles, men and women were represented across the range of LFGI scores in both 1997 and 2014. Correlations indicated no redundancy between measures used to calculate LFGI scores. Between 1997 and 2014 LFGI scores increased marginally for men and decreased marginally for women. However, LFGI scores among women were still more than 1.5 points higher on average than for men in 2014. Conclusions We have described and applied a method to create a measure of gender roles using survey data, where no direct measure of gender (masculinity/femininity) was available. This measure showed good variation among both men and women, and was responsive to change over time. The article concludes by outlining an approach to use this measure to examine the relative contribution of gender and sex on differences in health status (or other outcomes) between men and women. © The Author(s). 2016
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title_short	Measuring gender when you don’t have a gender measure: constructing a gender index using survey data
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Measuring gender when you don’t have a gender measure: constructing a gender index using survey data

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