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1.	Angle, John
1.	Work and Earnings: Cumulative Experience Method of Analysis of Longitudinal Surveys Sociological Methods and Research 8,2 (November 1979): 209-232. Also: http://smr.sagepub.com/content/8/2/209.abstract Cohort(s): Young Men Publisher: Sage Publications Keyword(s): Earnings; Life Cycle Research; Schooling; Work Experience Permission to reprint the abstract has not been received from the publisher. Conventional methods of model construction and testing are not well suited to the features of large longitudinal survey data sets. Conventional methods assume (1) equal time intervals between observations, (2) simultaneous observations, and (3) that missing observations are rare. As a result, the analysis of whole multiwave sets of longitudinal surveys becomes virtually impossible as the num ber of waves increases. This paper poses a research question about how a per son's work experience affects his or her earnings and shows how the Cumulative Experience Method (CEM) can provide an answer to the question using all available information in a longitudinal data set. CEM interpolates a person's experience between observation points and weights these inferred observations by the inverse of their expected error. The linear interpolation and weighting procedure of CEM accommodates easily to missing observations where these occur between earlier and later observations.
	Bibliography Citation Angle, John. "Work and Earnings: Cumulative Experience Method of Analysis of Longitudinal Surveys." Sociological Methods and Research 8,2 (November 1979): 209-232.

2.	Cherlin, Andrew J. Horiuchi, Shiro
2.	Retrospective Reports of Family Structure: A Methodological Assessment Sociological Methods and Research 8,4 (May 1980): 454-469. Also: http://smr.sagepub.com/content/8/4/454.abstract Cohort(s): Young Women Publisher: Sage Publications Keyword(s): Data Quality/Consistency; Family Structure; Household Models; Research Methodology Permission to reprint the abstract has not been received from the publisher. The authors investigate response inconsistencies in regard to a national panel of young women who were asked: "With whom were you living when you were 14 years old?" The findings show that there is considerable inconsistency between l968 and l972 as to whether or not the respondent said in 1968 that at age 14 she was living with both parents. The authors suggest that some of the respondent's households may have changed composition so there may not have been a single, true answer to the question. In addition, the authors hypothesize that others changed their responses to fit with what they viewed as soundly more acceptable responses. Despite the inconsistency between 1968 and 1972, the responses lead to similar conclusions when they were used in multivariate analyses.
	Bibliography Citation Cherlin, Andrew J. and Shiro Horiuchi. "Retrospective Reports of Family Structure: A Methodological Assessment." Sociological Methods and Research 8,4 (May 1980): 454-469.

3.	Hayward, Mark D. Lichter, Daniel T.
3.	A Life Cycle Model of Labor Force Inequality: Extending Clogg's Life Table Approach Sociological Methods and Research 26,4 (May 1998): 487-510. Also: http://smr.sagepub.com/content/26/4/487.abstract Cohort(s): Older Men Publisher: Sage Publications Keyword(s): Education; Income Dynamics/Shocks; Labor Force Participation; Life Cycle Research; Markov chain / Markov model; Statistical Analysis; Wage Gap Permission to reprint the abstract has not been received from the publisher. Develops explicit life-cycle measures of inequality that summarize the divergent stochastic processes defining group differences in labor force behavior, focusing on educational differences in individuals' work & retirement experiences over the latter part of the career cycle. The analytic approach is a Markov-based multistate life-table, directly extending Clifford C. Clogg's (1979) life-table model of labor force inequality. Analyses are based on 1966-1983 data from the National Longitudinal Survey of Older Men (initial N = 5,020 men, ages 45-59). The approach demonstrates how both prevalence measures of inequality and measures of life-cycle inequality are generated by the underlying stochastic processes. Comparisons of the life-cycle and prevalence measures illustrate the potentially divergent pictures of labor force inequality conveyed by the alternative measures. 5 Tables, 2 Figures, 20 References. Adapted from the source document
	Bibliography Citation Hayward, Mark D. and Daniel T. Lichter. "A Life Cycle Model of Labor Force Inequality: Extending Clogg's Life Table Approach." Sociological Methods and Research 26,4 (May 1998): 487-510.

4.	Hollister, Matissa
4.	Is Optimal Matching Suboptimal? Sociological Methods and Research 38,2 (November 2009): 235-264. Also: http://smr.sagepub.com/cgi/content/abstract/38/2/235 Cohort(s): NLSY79 Publisher: Sage Publications Keyword(s): Career Patterns; Intergenerational Patterns/Transmission; Job Patterns; Occupational Status; Occupations; Work Histories Permission to reprint the abstract has not been received from the publisher. Optimal matching (OM) is a method for measuring the similarity between pairs of sequences (e.g., work histories). This article discusses two problems with optimal matching. First, the author identifies a flaw in OM "indel costs" and proposes a solution to this flaw. Second, the author discusses the need for benchmarks to measure the added value of OM and to test competing versions. To that end, the author conducts an empirical test of traditional OM, the alternative localized OM, and sequence comparison. The test documents the problem with traditional OM and shows that it is solved by localized OM. The test also demonstrates the value of OM and sequence comparison in examining occupational sequences; both methods capture variation beyond traditional human capital and status attainment measures, although the marginal improvements of OM over sequence comparison may not justify its computational complexity. These results point to the need for more systematic approaches to sequence analysis methods.
	Bibliography Citation Hollister, Matissa. "Is Optimal Matching Suboptimal?" Sociological Methods and Research 38,2 (November 2009): 235-264.

5.	Holm, Anders Hjorth-Trolle, Anders Andersen, Robert
5.	Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate Holm, A., Hjorth-Trolle, A., & Andersen, R. (2023). Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate. Sociological Methods & Research, 0(0). Also: https://doi.org/10.1177/00491241231176845 Cohort(s): Children of the NLSY79 Publisher: Sage Publications Keyword(s): Methods/Methodology; Peabody Individual Achievement Test (PIAT- Reading); Statistics Permission to reprint the abstract has not been received from the publisher. Lagged dependent variables (LDVs) are often used as predictors in ordinary least squares (OLS) models in the social sciences. Although several estimators are commonly employed, little is known about their relative merits in the presence of classical measurement error and different longitudinal processes. We assess the performance of four commonly used estimators: (1) the standard OLS estimator, (2) an average of past measures (AVG), (3) an instrumental variable (IV) measured at one period previously (IV), and (4) an IV derived from information from more than one time before (IV2). We also propose a new estimator for fixed effects models—the first difference instrumental variable (FDIV) estimator. After exploring the consistency of these estimators, we demonstrate their performance using an empirical application predicting primary school test scores. Our results demonstrate that for a Markov process with classic measurement error (CME), IV and IV2 estimators are generally consistent; LDV and AVG estimators are not. For a semi-Markov process, only the IV2 estimator is consistent. On the other hand, if fixed effects are included in the model, only the FDIV estimator is consistent. We end with advice on how to select the appropriate estimator.
	Bibliography Citation Holm, Anders, Anders Hjorth-Trolle and Robert Andersen. "Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate." Holm, A., Hjorth-Trolle, A., & Andersen, R. (2023). Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate. Sociological Methods & Research, 0(0).

6.	Lynch, Scott M. Western, Bruce
6.	Bayesian Posterior Predictive Checks for Complex Models Sociological Methods and Research 32,3 (February 2004): 301-335. Also: http://smr.sagepub.com/content/32/3/301.abstract Cohort(s): NLSY79 Publisher: Sage Publications Keyword(s): Bayesian; Modeling, Fixed Effects; Modeling, Mixed Effects; Modeling, Multilevel; Modeling, Probit; Modeling, Random Effects Permission to reprint the abstract has not been received from the publisher. In sociological research, it is often difficult to compare nonnested models and to evaluate the fit of models in which outcome variables are not normally distributed. In this article, the authors demonstrate the utility of Bayesian posterior predictive distributions specif-ically, as well as a Bayesian approach to modeling more generally, in tackling these issues. First, they review the Bayesian approach to statistics and computation. Second, they discuss the evaluation of model fit in a bivariate probit model. Third, they discuss comparing fixed- and random-effects hierarchical linear models. Both examples high-light the use of Bayesian posterior predictive distributions beyond these particular cases. Copyright: 2004 Sage Publications
	Bibliography Citation Lynch, Scott M. and Bruce Western. "Bayesian Posterior Predictive Checks for Complex Models." Sociological Methods and Research 32,3 (February 2004): 301-335.

7.	Moore, Kristin Anderson Halle, Tamara G. Vandivere, Sharon Mariner, Carrie L.
7.	Scaling Back Survey Scales: How Short is too Short? Sociological Methods and Research 30,4 (May 2002): 530-567. Also: http://smr.sagepub.com/cgi/content/abstract/30/4/530 Cohort(s): Children of the NLSY79 Publisher: Sage Publications Keyword(s): Behavior Problems Index (BPI); Child Development; Data Quality/Consistency; Home Observation for Measurement of Environment (HOME); Parent-Child Relationship/Closeness; Peabody Individual Achievement Test (PIAT- Reading); Scale Construction; Test Scores/Test theory/IRT Permission to reprint the abstract has not been received from the publisher. To understand children's development, one must examine an array of constructs. Yet the time and budget constraints of large-scale survey research create a dilemma: how to cut scales to the fewest possible items while still retaining their predictive properties. In this article, the authors compare the predictive validity of several shortened versions of the Behavior Problems Index and the Home Observation for Measurement of the Environment--Short Form with their full scales within the National Longitudinal Survey of Youth--1979 cohort. They use the scales to predict delinquency, reading recognition scores, parent-child activities, and smoking behavior of 2,017 children at ages 13 or 14 from data gathered 2, 4, and 6 years prior. Analyses leave the authors cautiously optimistic that short scales, especially scales composed of items gathered at different time points and repeated regularly, may enjoy substantial predictive power. However, two-item scales may be too short, and psychometric study on additional scales is warranted. (PsycINFO Database Record Copyright: 2002 APA, all rights reserved)
	Bibliography Citation Moore, Kristin Anderson, Tamara G. Halle, Sharon Vandivere and Carrie L. Mariner. "Scaling Back Survey Scales: How Short is too Short?" Sociological Methods and Research 30,4 (May 2002): 530-567.

8.	Powers, Daniel A.
8.	Assessing Group Differences in Estimated Baseline Survivor Functions From Cox Proportional Hazards Models Sociological Methods and Research 39,2 (November 2010): 157-187. Also: http://smr.sagepub.com/content/39/2/157.abstract Cohort(s): NLSY79 Publisher: Sage Publications Keyword(s): Modeling, Hazard/Event History/Survival/Duration; Pregnancy, Adolescent; Religion Permission to reprint the abstract has not been received from the publisher. The author discusses the general problem of evaluating differences in adjusted survivor functions and develops a heuristic approach to generate the expected events that would occur under a Cox proportional hazards model. Differences in the resulting expected survivor distributions can be tested using generalized log rank tests. This method should prove useful for making other kinds of comparisons and generating adjusted life tables. The author also discusses alternative specifications of the classical Cox model that allow time-varying effects and thus permit a more direct assessment of group differences at various points in time. He implements recently developed semiparametric approaches for estimating time-varying effects, which permit statistical tests of group difference in effects as well as tests of time-invariant effects. He shows that these approaches can provide insight into the nature of time-varying effects and can help reveal the temporal dynamic of group differences.
	Bibliography Citation Powers, Daniel A. "Assessing Group Differences in Estimated Baseline Survivor Functions From Cox Proportional Hazards Models." Sociological Methods and Research 39,2 (November 2010): 157-187.

9.	Rosenfeld, Rachel A. Nielsen, Francois
9.	Inequality and Careers: A Dynamic Model of Socioeconomic Achievement Sociological Methods and Research 12,3 (February 1984): 279-321. Also: http://smr.sagepub.com/content/12/3/279.abstract Cohort(s): Young Men, Young Women Publisher: Sage Publications Keyword(s): Career Patterns; Discrimination, Sex; Occupational Attainment; Wages; Work History Permission to reprint the abstract has not been received from the publisher. Socioeconomic careers involve a process of attainment. One model that explicitly recognizes this is a simple linear differential equation model. This article shows how such a model can be used to describe careers in terms of entry levels and their determinants, potential levels and their determinants, and the rate of achievement. Such models, while conceptually simple, have some statistical complications over usual models used when it comes time to estimate them. The second half of the article describes some of these complications and ways of dealing with them.
	Bibliography Citation Rosenfeld, Rachel A. and Francois Nielsen. "Inequality and Careers: A Dynamic Model of Socioeconomic Achievement." Sociological Methods and Research 12,3 (February 1984): 279-321.

10.	Ruttenauer, Tobias Ludwig, Volker
10.	Fixed Effects Individual Slopes: Accounting and Testing for Heterogeneous Effects in Panel Data or Other Multilevel Models Sociological Methods and Research published online (10 June 2020): DOI: 10.1177/0049124120926211 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Sage Publications Keyword(s): Head Start; Marital Status; Modeling, Fixed Effects; Monte Carlo; Research Methodology; Wage Dynamics Permission to reprint the abstract has not been received from the publisher. Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that are related to the parameter of interest (e.g., selection into treatment is based on individual growth of the outcome). In this study, we derive the bias in conventional FE models and show that fixed effects individual slope (FEIS) models can overcome this problem. This is a more general version of the conventional FE model, which accounts for heterogeneous slopes or trends, thereby providing a powerful tool for panel data and other multilevel data in general. We propose two versions of the Hausman test that can be used to identify misspecification in FE models. The performance of the FEIS estimator and the specification tests is evaluated in a series of Monte Carlo experiments. Using the examples of the marital wage premium and returns to preschool education (Head Start), we demonstrate how taking heterogeneous effects into account can seriously change the conclusions drawn from conventional FE models. Thus, we propose to test for bias in FE models in practical applications and to apply FEIS if indicated by the specification tests.
	Bibliography Citation Ruttenauer, Tobias and Volker Ludwig. "Fixed Effects Individual Slopes: Accounting and Testing for Heterogeneous Effects in Panel Data or Other Multilevel Models." Sociological Methods and Research published online (10 June 2020): DOI: 10.1177/0049124120926211.

11.	Schneider, Daniel J. Harknett, Kristen S.
11.	What's to Like? Facebook as a Tool for Survey Data Collection Sociological Methods and Research published online (14 November 2019): DOI: 10.1177/0049124119882477. Also: https://journals.sagepub.com/doi/full/10.1177/0049124119882477 Cohort(s): NLSY97 Publisher: Sage Publications Keyword(s): Comparison Group (Reference group); Current Population Survey (CPS) / CPS-Fertility Supplement; Data Quality/Consistency; Job Tenure; Wages Permission to reprint the abstract has not been received from the publisher. In this article, we explore the use of Facebook targeted advertisements for the collection of survey data. We illustrate the potential of survey sampling and recruitment on Facebook through the example of building a large employee-employer linked data set as part of The Shift Project. We describe the workflow process of targeting, creating, and purchasing survey recruitment advertisements on Facebook. We address concerns about sample selectivity and apply poststratification weighting techniques to adjust for differences between our sample and that of "gold standard" data sources. We then compare univariate and multivariate relationships in the Shift data against the Current Population Survey and the National Longitudinal Survey of Youth 1997. Finally, we provide an example of the utility of the firm-level nature of the data by showing how firm-level gender composition is related to wages. We conclude by discussing some important remaining limitations of the Facebook approach, as well as highlighting some unique strengths of the Facebook targeted advertisement approach, including the ability for rapid data collection in response to research opportunities, rich and flexible sample targeting capabilities, and low cost, and we suggest broader applications of this technique.
	Bibliography Citation Schneider, Daniel J. and Kristen S. Harknett. "What's to Like? Facebook as a Tool for Survey Data Collection." Sociological Methods and Research published online (14 November 2019): DOI: 10.1177/0049124119882477.

12.	Scott, Marc A. Handcock, Mark S.
12.	Persistent Inequality? Answers From Hybrid Models for Longitudinal Data Sociological Methods and Research 34,1 (August 2005): 3-30. Also: http://smr.sagepub.com/content/34/1/3.abstract Cohort(s): NLSY79 Publisher: Sage Publications Keyword(s): Income Distribution; Intergenerational Patterns/Transmission; Wage Dynamics; Wealth Permission to reprint the abstract has not been received from the publisher. Many questions in social research must be evaluated over time. For example, in studies of intragenerational mobility, measuring opportunity for economic advancement requires longitudinal data. The authors develop and use a class of hybrid functional models to demonstrate how different models can lead to extremely different substantive conclusions. They provide guidelines for longitudinal data analyses in which variance partitions are central to the inquiry. In their analysis of the National Longitudinal Survey of Youth, the authors conclude that in a period of rising wage dispersion, the bulk of inequality is persistent over the life course. Their models provide support for the scenario in which wage inequality rises steadily while instability slowly diminishes over time. They obtain mild evidence of increased wage instability for somewhat older workers in the early 1990s, matching a recessionary trend. These findings contribute significantly to understanding wage inequality in United States over the past 25 years. [ABSTRACT FROM AUTHOR]
	Bibliography Citation Scott, Marc A. and Mark S. Handcock. "Persistent Inequality? Answers From Hybrid Models for Longitudinal Data." Sociological Methods and Research 34,1 (August 2005): 3-30.

13.	Vuolo, Mike
13.	Copula Models for Sociology: Measures of Dependence and Probabilities for Joint Distributions Sociological Methods and Research 46,3 (August 2017): 604-648. Also: http://journals.sagepub.com/toc/smra/46/3 Cohort(s): NLSY97 Publisher: Sage Publications Keyword(s): Alcohol Use; Grade Point Average (GPA)/Grades; Modeling; Statistical Analysis Permission to reprint the abstract has not been received from the publisher. Often in sociology, researchers are confronted with nonnormal variables whose joint distribution they wish to explore. Yet, assumptions of common measures of dependence can fail or estimating such dependence is computationally intensive. This article presents the copula method for modeling the joint distribution of two random variables, including descriptions of the method, the most common copula distributions, and the nonparametric measures of association derived from the models. Copula models, which are estimated by standard maximum likelihood techniques, make no assumption about the form of the marginal distributions, allowing consideration of a variety of models and distributions in the margins and various shapes for the joint distribution. The modeling procedure is demonstrated via a simulated example of spousal mortality and empirical examples of (1) the association between unemployment and suicide rates with time series models and (2) the dependence between a count variable (days drinking alcohol) and a skewed, continuous variable (grade point average) while controlling for predictors of each using the National Longitudinal Survey of Youth 1997. Other uses for copulas in sociology are also described.
	Bibliography Citation Vuolo, Mike. "Copula Models for Sociology: Measures of Dependence and Probabilities for Joint Distributions." Sociological Methods and Research 46,3 (August 2017): 604-648.

14.	Wang, Xiaoqing Wu, Haotian Feng, Xiangnan Song, Xinyuan
14.	Bayesian Two-level Model for Repeated Partially Ordered Responses: Application to Adolescent Smoking Behavior Analysis Sociological Methods and Research published online (5 March 2019): DOI: 10.1177/0049124119826149. Also: https://journals.sagepub.com/doi/full/10.1177/0049124119826149 Cohort(s): NLSY97 Publisher: Sage Publications Keyword(s): Adolescent Behavior; Bayesian; Monte Carlo; Smoking (see Cigarette Use) Permission to reprint the abstract has not been received from the publisher. Given the questionnaire design and the nature of the problem, partially ordered data that are neither completely ordered nor completely unordered are frequently encountered in social, behavioral, and medical studies. However, early developments in partially ordered data analysis are very limited and restricted only to cross-sectional data. In this study, we propose a Bayesian two-level regression model for analyzing repeated partially ordered responses in longitudinal data. The first-level model is defined for partially ordered observations of interest that are taken at each time point nested within individuals, while the second-level model is defined for individuals to assess the effects of their characteristics on the first-level model. A full Bayesian approach with the Markov chain Monte Carlo algorithm is developed for statistical inference. Simulation studies demonstrate the satisfactory performance of the developed methodology. The methodology is then applied to a longitudinal study on adolescent smoking behavior.
	Bibliography Citation Wang, Xiaoqing, Haotian Wu, Xiangnan Feng and Xinyuan Song. "Bayesian Two-level Model for Repeated Partially Ordered Responses: Application to Adolescent Smoking Behavior Analysis." Sociological Methods and Research published online (5 March 2019): DOI: 10.1177/0049124119826149.

15.	Zhou, Xiang
15.	Attendance, Completion, and Heterogeneous Returns to College: A Causal Mediation Approach Sociological Methods and Research published online (1 August 2022): DOI: 10.1177/00491241221113876. Also: https://journals.sagepub.com/doi/full/10.1177/00491241221113876 Cohort(s): NLSY97 Publisher: Sage Publications Keyword(s): College Education; Disadvantaged, Economically; Earnings; Educational Returns; Heterogeneity Permission to reprint the abstract has not been received from the publisher. A growing body of social science research investigates whether the economic payoff to a college education is heterogeneous -- in particular, whether disadvantaged youth can benefit more from attending and completing college relative to their more advantaged peers. Scholars, however, have employed different analytical strategies and reported mixed findings. To shed light on this literature, I propose a causal mediation approach to conceptualizing, evaluating, and unpacking the causal effects of college on earnings. By decomposing the total effect of attending a four-year college into several direct and indirect components, this approach not only clarifies the mechanisms through which college attendance boosts earnings, but illuminates the ways in which the postsecondary system may be both an equalizer and a stratifier. The total effect of college attendance, its direct and indirect components, and their heterogeneity across different subpopulations are all identified under the assumption of sequential ignorability. I introduce a debiased machine learning (DML) method for estimating all quantities of interest, along with a set of bias formulas for sensitivity analysis. I illustrate the proposed framework and methodology using data from the National Longitudinal Survey of Youth, 1997 cohort.
	Bibliography Citation Zhou, Xiang. "Attendance, Completion, and Heterogeneous Returns to College: A Causal Mediation Approach." Sociological Methods and Research published online (1 August 2022): DOI: 10.1177/00491241221113876.

16.	Zhou, Xiang Xie, Yu
16.	Propensity Score-based Methods Versus MTE-based Methods in Causal Inference: Identification, Estimation, and Application Sociological Methods and Research 45,1 (February 2016): 3-40. Also: http://smr.sagepub.com/content/45/1/3 Cohort(s): NLSY79 Publisher: Sage Publications Keyword(s): College Education; Educational Returns; Modeling, Instrumental Variables; Propensity Scores Permission to reprint the abstract has not been received from the publisher. Since the seminal introduction of the propensity score (PS) by Rosenbaum and Rubin, PS-based methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the PS approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For situations where this assumption may be violated, Heckman and his associates have recently developed a novel approach based on marginal treatment effects (MTEs). In this article, we (1) explicate the consequences for PS-based methods when aspects of the ignorability assumption are violated, (2) compare PS-based methods and MTE-based methods by making a close examination of their identification assumptions and estimation performances, (3) apply these two approaches in estimating the economic return to college using data from the National Longitudinal Survey of Youth (NLSY) of 1979 and discuss their discrepancies in results. When there is a sorting gain but no systematic baseline difference between treated and untreated units given observed covariates, PS-based methods can identify the treatment effect of the treated (TT). The MTE approach performs best when there is a valid and strong instrumental variable (IV). In addition, this article introduces the "smoothing-difference PS-based method," which enables us to uncover heterogeneity across people of different PSs in both counterfactual outcomes and treatment effects.
	Bibliography Citation Zhou, Xiang and Yu Xie. "Propensity Score-based Methods Versus MTE-based Methods in Causal Inference: Identification, Estimation, and Application." Sociological Methods and Research 45,1 (February 2016): 3-40.

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