Pub Date : 2025-12-17DOI: 10.1177/00491241251405869
Christopher Bratt
Can aggregated composite scores be used to compare countries or other groups despite measurement non-invariance? We propose a pragmatic approach, emphasizing that measurement invariance is valuable but not strictly necessary for all such comparisons. For descriptive analyses of group differences, composite scores may outperform factor-analytic approaches, because they are more intuitive and can capture multiple dimensions. Using data from the European Social Survey (39 countries, 11 measurement occasions, 546,954 respondents), we examined social and political trust. Composite scores aggregated to the country level were practically indistinguishable from countries’ factor scores based on approximate measurement invariance testing. We conclude that composite scores can suffice for simple group comparisons, though their suitability depends on the data. They can, however, underestimate uncertainty, producing overly narrow confidence intervals. We further show that measurement invariance does not guarantee measurement equivalence. Finally, we highlight how researchers can leverage data even if measurement invariance fails.
{"title":"Benefits of a Pragmatic Approach: Rethinking Measurement Invariance and Composite Scores in Cross-Cultural Research","authors":"Christopher Bratt","doi":"10.1177/00491241251405869","DOIUrl":"https://doi.org/10.1177/00491241251405869","url":null,"abstract":"Can aggregated composite scores be used to compare countries or other groups despite measurement non-invariance? We propose a pragmatic approach, emphasizing that measurement invariance is valuable but not strictly necessary for all such comparisons. For descriptive analyses of group differences, composite scores may outperform factor-analytic approaches, because they are more intuitive and can capture multiple dimensions. Using data from the European Social Survey (39 countries, 11 measurement occasions, 546,954 respondents), we examined social and political trust. Composite scores aggregated to the country level were practically indistinguishable from countries’ factor scores based on approximate measurement invariance testing. We conclude that composite scores can suffice for simple group comparisons, though their suitability depends on the data. They can, however, underestimate uncertainty, producing overly narrow confidence intervals. We further show that measurement invariance does not guarantee measurement equivalence. Finally, we highlight how researchers can leverage data even if measurement invariance fails.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"163 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1177/00491241251403078
Ingrid Mauerer, Gerhard Tutz
We present an item response model for ordinal public opinion data to understand individual-level variation in attitudes as a function of covariates. The approach allows investigating how individuals (or population subgroups) differ in substantive stances and attitude strength. It is a two-dimensional partial credit model that incorporates covariates linked to attitude direction and strength into the basic model. We exemplify the types of substantive insights into heterogeneity that can be obtained from the approach but not from existing models with two applications: attitudes toward gender equality (European Values Study) and the evaluation of presidential candidates (American National Election Study).
{"title":"An Ordinal Item Response Model for Understanding Attitudes","authors":"Ingrid Mauerer, Gerhard Tutz","doi":"10.1177/00491241251403078","DOIUrl":"https://doi.org/10.1177/00491241251403078","url":null,"abstract":"We present an item response model for ordinal public opinion data to understand individual-level variation in attitudes as a function of covariates. The approach allows investigating how individuals (or population subgroups) differ in substantive stances and attitude strength. It is a two-dimensional partial credit model that incorporates covariates linked to attitude direction and strength into the basic model. We exemplify the types of substantive insights into heterogeneity that can be obtained from the approach but not from existing models with two applications: attitudes toward gender equality (European Values Study) and the evaluation of presidential candidates (American National Election Study).","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1177/00491241251398031
Laura López-Pérez, Mayra Ortiz Ocaña
In the social sciences, most process tracing evidence is gathered through individual or atomized sources. However, there are some cases in which individualized data collection methods are not enough to capture collective social processes. We propose using focus groups for process tracing (FGFPT) to gather and analyze qualitative evidence about causal processes and mechanisms by leveraging interaction and discussions. We present three key benefits of using FGFPT: instant fact-checking, obtaining mechanistic evidence through the interactive process, and enhancing participants’ collective agency. Additionally, we propose general guidelines for designing and implementing focus groups with the aim of process tracing: specifying observable implications, forming the focus group, question design, and training the moderator. Focus groups can be the most adequate data collection method to support and enhance process tracing exercises for collective phenomena.
{"title":"Using Focus Groups for Process Tracing: Leveraging Group Discussions for Causal Inference","authors":"Laura López-Pérez, Mayra Ortiz Ocaña","doi":"10.1177/00491241251398031","DOIUrl":"https://doi.org/10.1177/00491241251398031","url":null,"abstract":"In the social sciences, most process tracing evidence is gathered through individual or atomized sources. However, there are some cases in which individualized data collection methods are not enough to capture collective social processes. We propose using focus groups for process tracing (FGFPT) to gather and analyze qualitative evidence about causal processes and mechanisms by leveraging interaction and discussions. We present three key benefits of using FGFPT: instant fact-checking, obtaining mechanistic evidence through the interactive process, and enhancing participants’ collective agency. Additionally, we propose general guidelines for designing and implementing focus groups with the aim of process tracing: specifying observable implications, forming the focus group, question design, and training the moderator. Focus groups can be the most adequate data collection method to support and enhance process tracing exercises for collective phenomena.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"28 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-17DOI: 10.1177/00491241251376509
Bent Nielsen
Age-period-cohort analysis is often done in the context of two samples. This could be samples for women and men or for two countries. It is of interest to ask if some time effects could be common across samples. We clarify how the well-known age-period-cohort problem for one sample carries over to the two sample situation. This is done through a reparametrization in terms of parameters that are invariant to the identification issues. The new parametrization shows which hypotheses can be tested and their degrees of freedom. Testable hypotheses can be formulated for non-linear effects, but not for the linear parts of the individual time effects. This conclusion remains when imposing cross-sample restrictions. The analysis is extended to the mixed frequency situation where age and period are measured at different scales. As an empirical illustration a study of Swiss suicide rates is revisited.
{"title":"Two-sample Age-period-cohort Models","authors":"Bent Nielsen","doi":"10.1177/00491241251376509","DOIUrl":"https://doi.org/10.1177/00491241251376509","url":null,"abstract":"Age-period-cohort analysis is often done in the context of two samples. This could be samples for women and men or for two countries. It is of interest to ask if some time effects could be common across samples. We clarify how the well-known age-period-cohort problem for one sample carries over to the two sample situation. This is done through a reparametrization in terms of parameters that are invariant to the identification issues. The new parametrization shows which hypotheses can be tested and their degrees of freedom. Testable hypotheses can be formulated for non-linear effects, but not for the linear parts of the individual time effects. This conclusion remains when imposing cross-sample restrictions. The analysis is extended to the mixed frequency situation where age and period are measured at different scales. As an empirical illustration a study of Swiss suicide rates is revisited.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"6 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145311023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1177/00491241251379459
Artur Pokropek, Tomasz Żółtak, Eldad Davidov, Bart Meuleman, Peter Schmidt
Multilevel modelling (MM) is widely utilized in the social sciences, with over 20% of articles in leading sociological journals employing this technique. Despite its prevalence, few studies address whether the variables used in MM are invariant across groups or allow to construct reliable indicators. This study investigates the effects of both measurement noninvariance and random measurement error on MM using Monte Carlo simulations. Our findings reveal significant biases in MM results when random measurement errors are overlooked. Attaining high reliability in the indicators – above 0.94 – can mitigate these biases. While measurement noninvariance introduces bias in MM, its impact is smaller compared to that of the bias caused by unaddressed measurement error. Multilevel structural equation modelling (SEM), which controls for random measurement errors, performs effectively in complete measurement invariance (MI) scenarios. However, the absence of MI can create significant challenges. While multilevel SEM is a powerful analytical tool, it is not immune to the effects of MI assumption violations.
{"title":"Challenges in Multilevel Modelling: Cross-Group Measurement Noninvariance and Measurement Errors. A Monte Carlo Simulation Study","authors":"Artur Pokropek, Tomasz Żółtak, Eldad Davidov, Bart Meuleman, Peter Schmidt","doi":"10.1177/00491241251379459","DOIUrl":"https://doi.org/10.1177/00491241251379459","url":null,"abstract":"Multilevel modelling (MM) is widely utilized in the social sciences, with over 20% of articles in leading sociological journals employing this technique. Despite its prevalence, few studies address whether the variables used in MM are invariant across groups or allow to construct reliable indicators. This study investigates the effects of both measurement noninvariance and random measurement error on MM using Monte Carlo simulations. Our findings reveal significant biases in MM results when random measurement errors are overlooked. Attaining high reliability in the indicators – above 0.94 – can mitigate these biases. While measurement noninvariance introduces bias in MM, its impact is smaller compared to that of the bias caused by unaddressed measurement error. Multilevel structural equation modelling (SEM), which controls for random measurement errors, performs effectively in complete measurement invariance (MI) scenarios. However, the absence of MI can create significant challenges. While multilevel SEM is a powerful analytical tool, it is not immune to the effects of MI assumption violations.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"100 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145311024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-04DOI: 10.1177/00491241251358895
Ethan Fosse, Fabian T. Pfeffer
Over the past decade there has been a striking increase in the number of quantitative studies examining the effects of social mobility, with almost all based on the diagonal reference model (DRM). We make four main contributions to this rapidly expanding literature. First, we show that under plausible values of mobility effects, the DRM will, in many cases, implicitly force the underlying mobility linear effect toward zero. In addition, we show both mathematically and through simulations that the mobility effects estimated by the DRM are sensitive to the size and sign of the origin and destination linear effects, often in ways that are unlikely to be intuitive to applied researchers. This finding clarifies why, contrary to expectations, applied researchers have generally found mixed evidence of mobility effects. Second, we generalize the identification problem of conventional mobility effect models by showing that the DRM and related methods can be viewed as special cases of a bounding analysis, where identification is achieved by invoking extremely strong assumptions. Finally, and importantly, we present a new framework for the analysis of mobility tables based on the identification and estimation of joint parameter sets, introducing what we call the structural and dynamic inequality model. We show that this model is fully identified, relies on much weaker assumptions than conventional models of mobility effects, and can be treated both as a descriptive model and, if additional assumptions are invoked, as a causal model. We conclude with an agenda for further research on the consequences of socioeconomic mobility.
{"title":"Beyond the Diagonal Reference Model: Critiques and New Directions in the Analysis of Mobility Effects","authors":"Ethan Fosse, Fabian T. Pfeffer","doi":"10.1177/00491241251358895","DOIUrl":"https://doi.org/10.1177/00491241251358895","url":null,"abstract":"Over the past decade there has been a striking increase in the number of quantitative studies examining the effects of social mobility, with almost all based on the diagonal reference model (DRM). We make four main contributions to this rapidly expanding literature. First, we show that under plausible values of mobility effects, the DRM will, in many cases, implicitly force the underlying mobility linear effect toward zero. In addition, we show both mathematically and through simulations that the mobility effects estimated by the DRM are sensitive to the size and sign of the origin and destination linear effects, often in ways that are unlikely to be intuitive to applied researchers. This finding clarifies why, contrary to expectations, applied researchers have generally found mixed evidence of mobility effects. Second, we generalize the identification problem of conventional mobility effect models by showing that the DRM and related methods can be viewed as special cases of a bounding analysis, where identification is achieved by invoking extremely strong assumptions. Finally, and importantly, we present a new framework for the analysis of mobility tables based on the identification and estimation of joint parameter sets, introducing what we call the structural and dynamic inequality model. We show that this model is fully identified, relies on much weaker assumptions than conventional models of mobility effects, and can be treated both as a descriptive model and, if additional assumptions are invoked, as a causal model. We conclude with an agenda for further research on the consequences of socioeconomic mobility.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"158 1","pages":"1339-1395"},"PeriodicalIF":6.3,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-03DOI: 10.1177/00491241251382457
Jin-young Choi, Rangmi Myung, Myoung-jae Lee
For causal effects of a binary treatment on a right-censored duration, the widely used proportional hazard contrasts are non-causal with unrealistic restrictions. This article proposes an alternative flexible causal approach, where we estimate the cumulative hazard, not the hazard itself, using an additive or “exponential-additive” specification with freely time-varying parameters. Our approach includes the proportional hazard as a highly special case that allows only monotonic survival probability ratios (SPR’s), while our approach allows any shape of SPR’s. An empirical analysis on recidivism using the duration until re-arrest after prison release on parole/probation is provided, where the SPR trajectory is not monotonic, but has an inverted-U shape over time.
{"title":"Causal Duration Analysis Based on Survival Probability Ratio","authors":"Jin-young Choi, Rangmi Myung, Myoung-jae Lee","doi":"10.1177/00491241251382457","DOIUrl":"https://doi.org/10.1177/00491241251382457","url":null,"abstract":"For causal effects of a binary treatment on a right-censored duration, the widely used proportional hazard contrasts are non-causal with unrealistic restrictions. This article proposes an alternative flexible causal approach, where we estimate the cumulative hazard, not the hazard itself, using an additive or “exponential-additive” specification with freely time-varying parameters. Our approach includes the proportional hazard as a highly special case that allows only monotonic survival probability ratios (SPR’s), while our approach allows any shape of SPR’s. An empirical analysis on recidivism using the duration until re-arrest after prison release on parole/probation is provided, where the SPR trajectory is not monotonic, but has an inverted-U shape over time.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"9 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-26DOI: 10.1177/00491241251379461
Cody Spence, James D. Bachmeier, Claire E. Altman, Jennifer Van Hook, Kendal Lowrey
Research on the stratifying effects of migration status has increased sharply in the last two decades, although efforts have been hampered by the near absence of representative data that include detailed migration status measures. Researchers have developed various statistical and logical imputation methods that have produced widely varying estimates. In this article, we introduce a new indicator of migration status constructed from two federal surveys matched to the Social Security Administration's Numident file, a database that includes all citizens and legal residents of the United States. In models predicting poverty, our measure produces estimates comparable to those based on respondents’ own self-reports, in one federal survey, of their migration status. Both the administrative and survey-based measures produce poverty gradients that diverge from those produced by logic-based measures. Our findings contribute to mounting evidence of bias in the use of certain kinds of logic-based algorithms to impute migration status and demonstrate the promise of administrative record linkages in migration status research.
{"title":"Migration Status Gradients in Immigrant Poverty: A Comparison of Imputation Methods","authors":"Cody Spence, James D. Bachmeier, Claire E. Altman, Jennifer Van Hook, Kendal Lowrey","doi":"10.1177/00491241251379461","DOIUrl":"https://doi.org/10.1177/00491241251379461","url":null,"abstract":"Research on the stratifying effects of migration status has increased sharply in the last two decades, although efforts have been hampered by the near absence of representative data that include detailed migration status measures. Researchers have developed various statistical and logical imputation methods that have produced widely varying estimates. In this article, we introduce a new indicator of migration status constructed from two federal surveys matched to the Social Security Administration's Numident file, a database that includes all citizens and legal residents of the United States. In models predicting poverty, our measure produces estimates comparable to those based on respondents’ own self-reports, in one federal survey, of their migration status. Both the administrative and survey-based measures produce poverty gradients that diverge from those produced by logic-based measures. Our findings contribute to mounting evidence of bias in the use of certain kinds of logic-based algorithms to impute migration status and demonstrate the promise of administrative record linkages in migration status research.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"65 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-26DOI: 10.1177/00491241251377068
Felix J. Clouth, Maarten J. Bijlsma, Steffen Pauws, Jeroen K. Vermunt
The parametric g-formula can be used to estimate causal effects of time-varying exposures on observable outcomes. It resolves intermediate confounding in such settings by specifying several parametric models, one each for every time-varying variable, and by performing micro-simulations. However, its restriction to applications with observable outcomes limits its usability for social sciences where variables of interest are often unobservable constructs. In such cases, measurement models are needed. We propose a new approach utilizing bias-adjusted three-step latent Markov models (LMMs) within the parametric g-formula. LMMs estimate the probability of membership in an unobservable state conditional on observed indicator variables. By replacing the parametric models in the g-formula with LMMs, micro-simulations are performed as usual to estimate a causal effect of the time-varying exposure. We illustrate this new approach by estimating the average treatment effect of unemployment on several unobservable mental health states utilizing longitudinal data from the Longitudinal Internet studies for the Social Sciences panel.
{"title":"Causal Inference for Latent Markov Models Using the Parametric G-Formula","authors":"Felix J. Clouth, Maarten J. Bijlsma, Steffen Pauws, Jeroen K. Vermunt","doi":"10.1177/00491241251377068","DOIUrl":"https://doi.org/10.1177/00491241251377068","url":null,"abstract":"The parametric g-formula can be used to estimate causal effects of time-varying exposures on observable outcomes. It resolves intermediate confounding in such settings by specifying several parametric models, one each for every time-varying variable, and by performing micro-simulations. However, its restriction to applications with observable outcomes limits its usability for social sciences where variables of interest are often unobservable constructs. In such cases, measurement models are needed. We propose a new approach utilizing bias-adjusted three-step latent Markov models (LMMs) within the parametric g-formula. LMMs estimate the probability of membership in an unobservable state conditional on observed indicator variables. By replacing the parametric models in the g-formula with LMMs, micro-simulations are performed as usual to estimate a causal effect of the time-varying exposure. We illustrate this new approach by estimating the average treatment effect of unemployment on several unobservable mental health states utilizing longitudinal data from the Longitudinal Internet studies for the Social Sciences panel.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"95 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1177/00491241251377741
Soojin Park, Suyeon Kang, Chioun Lee
Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are primarily based on binary risk factors, and no formal approach currently exists to benchmark the strength of omitted confounding using observed covariates for binary risk factors. To address this gap, we extend a simulation-based sensitivity analysis that simulates unmeasured confounders, addressing two sources of bias emerging from deriving OTRs and estimating individualized effects. Additionally, we propose a formal bounding strategy that benchmarks the strength of omitted confounding for binary risk factors. Using the High School Longitudinal Study 2009 (HSLS:09), we demonstrate this sensitivity analysis and benchmarking method.
{"title":"Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition With Individualized Interventions","authors":"Soojin Park, Suyeon Kang, Chioun Lee","doi":"10.1177/00491241251377741","DOIUrl":"https://doi.org/10.1177/00491241251377741","url":null,"abstract":"Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are primarily based on binary risk factors, and no formal approach currently exists to benchmark the strength of omitted confounding using observed covariates for binary risk factors. To address this gap, we extend a simulation-based sensitivity analysis that simulates unmeasured confounders, addressing two sources of bias emerging from deriving OTRs and estimating individualized effects. Additionally, we propose a formal bounding strategy that benchmarks the strength of omitted confounding for binary risk factors. Using the High School Longitudinal Study 2009 (HSLS:09), we demonstrate this sensitivity analysis and benchmarking method.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"38 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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