Marc Scott

Trends in Job Instability and Wages for Young Adult Men

Data and measurement problems have complicated the debate over trends in job instability in the United States. We compare two cohorts of young white men from the National Longitudinal Surveys (NLS), construct a rigorous measure of job change, and confirm earlier findings of a significant increase in job instability. We then benchmark the NLS against other main data sets in the field and conduct a thorough attrition analysis. Extending the analysis to wages, we find that the wage returns to job changing have both declined and become more unequal for young adults, mirroring trends in their long‐term wage growth.

Pitfalls in Pathways: Some Perspectives on Competing Risks Event History Analysis in Education Research.

A set of discrete-time methods for competing risks event history analysis is presented. The approach used is accessible to the practitioner and the article describes the strengths, weaknesses, and interpretation of both exploratory and model-based tools. These techniques are applied to the impact of “nontraditional” enrollment features (working, stopout, and delayed enrollment) on competing outcomes of sub-baccalaureate enrollment (degree attainment, transfer, and dropout). In this setting, model selection, estimation, and comparative inference are discussed and more general guidelines for each stage of analysis are provided. Substantively, the study finds that nontraditional enrollment features are associated with dramatic changes in expected outcomes.

Trends in dietary fat and high-fat food intakes from 1991 to 2008 in the Framingham Heart Study participants.

Few longitudinal studies carried out in US adults have evaluated long-term dietary fat intakes and compared them with the national recommendations during the two-decade period when the prevalence of obesity and insulin resistance increased substantively. In the present study, we examined trends in the intakes of dietary fats and rich dietary sources of fats in the Framingham Heart Study Offspring Cohort over a 17-year period. The cohort was established in 1971-75 with follow-up examinations being conducted approximately every 4 years. Dietary data were collected using a semi-quantitative FFQ beginning in 1991 (exam 5). We included 2732 adults aged ≥ 25 years with complete dietary data in at least three examinations from 1991 to 2008. Descriptive statistics were generated using SAS version 9.3, and a repeated-measures model was used to examine trends in macronutrient and food intakes using R. Over the 17 years of follow-up, the percentage of energy derived from total fat and protein increased (27·3-29·8% of energy and 16·8-18·0% of energy, respectively) and that derived from carbohydrate decreased (51·0-46·8% of energy; P-trend < 0·001). Increases in the percentage of energy derived from all fat subtypes were observed, except for that derived from trans-fats, which decreased over time (P-trend < 0·001). Trends were similar between the sexes, although women exhibited a greater increase in the percentage of energy derived from saturated fat and less reduction in the percentage of energy derived from trans-fats (P interaction < 0·05). Trends in fat intake were similar across the BMI categories. The number of weekly servings of cheese, eggs, ice cream desserts, nuts, butter and sausages/processed meats increased, whereas the intake of milk, margarine, poultry, confectioneries, chips and breads decreased (P-trend < 0·001). In this cohort of predominantly Caucasian older adults, the percentage of energy derived from dietary fats increased over time, but it remained within the national recommendations of less than 35 % of total energy, on average.

Racial and Gender Identity Among Black Adolescent Males: An Intersectionality Perspective

A considerable amount of social identity research has focused on race and racial identity, while gender identity, particularly among Black adolescents, remains underexamined. The current study used survey data from 183 Black adolescent males (13-16 years old) to investigate the development and relation between racial and gender identity centrality and private regard, and how these identities impact adjustment over time. It was found that dimensions of racial and gender identity were strongly correlated. Levels of racial centrality increased over time while gender centrality, and racial and gender private regard declined. In addition, racial and gender identity uniquely contributed to higher levels of psychological well-being and academic adjustment. These findings are discussed within the context of existing identity theories and intersectionality theory.

The Sage handbook of multilevel modeling

Notes on Contributors Preface Multilevel Modeling - Jeffrey S Simonoff, Marc A Scott and Brian D Marx PART ONE: MULTILEVEL MODEL SPECIFICATION AND INFERENCE The Multilevel Model Framework - Jeff Gill and Andrew Womack Multilevel Model Notation - Establishing the Commonalities - Marc A Scott, Patrick E Shrout and Sharon L Weinberg Likelihood Estimation in Multilevel Models - Harvey Goldstein Bayesian Multilevel Models - Ludwig Fahrmeir, Thomas Kneib, and Stefan Lang The Choice between Fixed and Random Effects - Zac Townsend,Jack Buckley, Masataka Harada and Marc A Scott Centering Predictors and Contextual Effects - Craig K Enders Model Selection for Multilevel Models - Russell Steele Generalized Linear Mixed Models - Overview - Geert Verbeke and Geert Molenberghs Longitudinal Data Modeling - Nan M Laird and Garrett M Fitzmaurice Complexities in Error Structures Within Individuals - Vicente Nunez-Anton and Dale L Zimmerman Design Considerations in Multilevel Studies - Gerard van Breukelen and Mirjam Moerbeek Multilevel Models and Causal Inference - Jennifer Hill PART TWO: VARIATIONS AND EXTENSIONS OF THE MULTILEVEL MODEL Multilevel Functional Data Analysis - Ciprian M Crainiceanu, Brian S Caffo and Jeffrey S Morris Nonlinear Models - Lang Wu and Wei Liu Generalized Linear Mixed Models: Estimation and Inference - Charles E McCulloch and John M Neuhaus Categorical Response Data - Jeroen Vermunt Smoothing and Semiparametric Models - Jin-Ting Zhang Penalized Splines and Multilevel Models - Goran Kauermann and Torben Kuhlenkasper Hierarchical Dynamic Models - Marina Silva Paez and Dani Gamerman Mixture and Latent Class Models in Longitudinal and Other Settings - Ryan P Browne and Paul D McNicholas Multivariate Response Data - Helena Geys and Christel Faes PART THREE: PRACTICAL CONSIDERATIONS IN MODEL FIT AND SPECIFICATION Robust Methods for Multilevel Analysis - Joop Hox and Rens van de Schoot Missing Data - Geert Molenberghs and Geert Verbeke Lack of Fit, Graphics, and Multilevel Model Diagnostics - Gerda Claeskens Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors? - Robert Crouchley Software for Fitting Multilevel Models - Andrzej T Galecki and Brady T West PART FOUR: SELECTED APPLICATIONS Meta-Analysis - Larry V Hedges and Kimberly S Maier Modeling Policy Adoption and Impact with Multilevel Methods - James E Monogan III Multilevel Models in the Social and Behavioral Sciences - David Rindskopf Survival Analysis and the Frailty Model: The effect of education on survival and disability for older men in England and Wales - Ardo van den Hout and Brian D M Tom Point-Referenced Spatial Modeling - Andrew O Finley and Sudipto Banerjee Market Research and Preference Data - Adam Sagan Multilevel Modeling for Scoial Networks and Relational Data - Marijtje A J Van Duijn Name Index Subject Index

Trends in dietary carbohydrate consumption from 1991 to 2008 in the Framingham Heart Study Offspring Cohort.

The intake of carbohydrates has been evaluated cross-sectionally, but not longitudinally in an ageing American adult population. The aim of the present study was to examine trends in the intake of dietary carbohydrates and their major food sources among the Framingham Heart Study Offspring (FOS) cohort, which had been uniquely tracked for 17 years in the study. The FOS cohort was recruited in 1971-1975. Follow-up examinations were conducted, on average, every 4 years. Dietary data collection began in 1991 (examination 5) using a validated semi-quantitative FFQ. The study included 2894 adults aged ≥ 25 years with complete dietary data in at least three examinations from 1991 to 2008. Descriptive statistics were generated using SAS version 9.3, and a repeated-measures model was used to examine trends in the intake of carbohydrates and their food sources in the whole sample, and by sex and BMI category. Over 17 years of follow-up, the percentage of energy from total carbohydrates (51·0-46·8 %; P for trend < 0·001) and total sugars (18·2-16·6 %; P for trend < 0·001) decreased. There was a decrease in the percentage of energy from fructose (5·4-4·7 %; P for trend < 0·001) and sucrose (9·8-8·8 %; P for trend < 0·001). Dietary fibre intake increased (18·0-19·2 g/d; P for trend < 0·001). The number of weekly servings of yeast bread, soft drinks/soda, cakes/cookies/quick breads/doughnuts, potatoes, milk, pasta, rice and cooked grains, fruit juice/drinks, potato chips/maize chips/popcorn, and lunch foods (e.g. pizzas and burgers) decreased significantly (P for trend < 0·001), while the intake of ready-to-eat cereals, legumes, fruits, dairy products, candy and ice cream/sherbet/frozen yogurt increased significantly (P for trend<0·04). Similar trends were observed when the analyses were stratified by sex and BMI. The present results suggest favourable trends in dietary carbohydrate consumption, but dietary guidelines for fruits, vegetables and fibre were not met in this cohort.

Early mother–child attachment and behavior problems in middle childhood: the role of the subsequent caregiving environment

The current study investigated associations between early mother–child attachment, as well as mother–child and teacher–child relationships, and internalizing and externalizing behaviors in middle childhood. Data from the NICHD Study of Early Child Care and Youth Development were used. Findings from a series of individual growth curve analyses revealed that attachment security was negatively related to internalizing and externalizing behaviors, while insecure/other and avoidant attachment were positively related to internalizing behaviors. In addition, longitudinal associations were found between mother–child and teacher–child relationships and internalizing and externalizing behaviors across middle childhood. Implications for attachment theory are discussed.

The Content of Their Coursework: Understanding Course-Taking Patterns at Community Colleges by Clustering Student Transcripts

Community college students typically have access to a large selection of courses and programs, and therefore the student transcripts at any one college or college system tend to be very diverse. As a result, it is difficult for faculty, administrators, and researchers to understand the course-taking patterns of students in order to determine what programs of study they appear to be pursuing. Attempting to examine these patterns and then comparing them with listed program requirements would be a very timeconsuming activity. The most common way of assigning a program of study to a student—picking the subject in which she has taken the most courses—is overly simple, because many programs require courses across several subjects. However, because students who have similar patterns of course-taking in terms of subjects and particular courses taken are likely to be in similar programs, clustering can be a useful way to make sense of the relevant data. Clustering allows researchers to group similar items into clusters, relying only on a measure of the similarity of those items. In this paper, we apply a clustering algorithm to the problem of understanding college transcripts, which serve as the items to be clustered. To our knowledge, this is the first effort to organize transcripts based on their course content using clustering. We base the measure of similarity on the proportion of curricular subjects that each transcript has in common with every other one. Our data are community and technical college transcripts for a cohort of students who first entered the Washington State system during the fall of the 2005–06 academic year and who had no prior postsecondary experience. We used our clustering algorithm to separately cluster liberal arts and career-technical students. We found that the algorithm did a good job of separately clustering each of these groups. The clusters roughly corresponded to programs of study, so we were able to estimate how many students were undertaking each program and what subjects students were studying within each cluster. We were also able to examine the demographics and the completion and transfer rates of the students within each cluster, in order to get an idea of what types of students were in each program of study and how successful they seemed to be in college. We found substantial variation on these dimensions as well as on the extent to which students’ programs were either concentrated in a single subject or spread across several subjects. Clustering is a powerful way to understand the course-taking patterns of students and assign programs of study. It makes few prior assumptions about the data; rather, it allows the data to organize itself based on a similarity measure. It relieves the analyst of determining what the program categories should be. It has the ability to detect patterns of activity across subjects within student transcripts. Note that although we have applied this method to community college students, it is applicable at all levels of postsecondary education. We conclude that this method would be useful to researchers throughout education who are trying to understand student course-taking patterns and programs of study, and who need to organize large amounts of transcript data.

Educational Outcomes of Postsecondary Occupational Students

This Brief discusses a research study that was conducted to determine the rates of degree completion of community college occupational students compared with other types of postsecondary students, and to identify factors that might explain those differences. Much of the existing literature on college persistence and completion is focused on baccalaureate students and pays little attention to students in community colleges, and even fewer studies consider differences by students’ program of study. Therefore, it is important to investigate whether students in occupational programs in community colleges persist in postsecondary education and attain degrees at similar rates as their academic and baccalaureate peers. Community college students, as defined here, are those taking for-credit courses at a two-year or less than two-year institution, or at a four-year institution, and who are pursuing a certificate or associate degree, or seeking no degree. Thus, community college student is a descriptive term independent of the type of institution the student is attending; rather, the designation is based on the student’s type of degree program. While we include some students at four-year institutions because of their stated degree objective, nearly 90 percent of all community college students fitting this definition attend two-year or less than two-year institutions, with more than threequarters attending public two-year institutions. Occupational students constitute a group within the community college student population whose self-reported major is in one of the following vocational fields of study: agricultural business and production, agricultural sciences, business, communication technologies, computer and information science, construction, engineering, engineering technologies, health professions, home economics, mechanics and repair, personal services, precision production, protective services, science technologies, or transportation. Academic students also comprise a group of community college students. Their self-reported major is in an academic field of study (humanities, mathematics, science, or social science). Baccalaureate students are those taking for-credit courses toward a bachelor’s degree at a four-year institution.

The Impact of Uncapping of Mandatory Retirement on Postsecondary Institutions

The Federal Age Discrimination in Employment Act passed by Congress in 1986 eliminated mandatory age-related retirement at age 70. Initially, all postsecondary institutions were exempt from the Act. Based on a report by the National Research Council (NRC), which forecast only a minimal impact of this Act on higher education, the federal government allowed the exemption to lapse; effective December 31, 1993, faculty would no longer be subject to mandatory retirement for age. Our results of an empirical analysis on nearly four decades of faculty data (from 1981 to 2009) from a large private metropolitan research university in the northeast contradicts that forecast and shows the extent to which faculty retirement behavior has changed following the enactment of the Act and the lapse of the exemption for higher education faculty. Although only 11% of faculty who were subject to mandatory retirement remained after age 70 (perhaps those with special arrangements), we find after the law changed that 60% of faculty no longer subject to mandatory retirement are expected to remain employed beyond age 70 and 15% will retire at age 80 or over. This is a dramatic shift in retirement behavior, one that was not forecast by the NRC committee. Our results also show how many years after the prior mandatory retirement age of 70, faculty now remain at their institutions. We also offer suggestions as to some of the potential reasons (consistent with the literature) why, since the change in the law, some faculty wait longer than others to retire, but we do so primarily to spur discourse, as these factors are based on our understanding of this institution. Our findings are limited to this single institution and do not imply a general trend for all postsecondary institutions. Additional studies are recommended to determine whether uncapping has had a similar effect at other postsecondary institutions.