Dynamic analysis of discrete dependent variables: a didactic essay

Zunehmend erforschen Sozialwissenschaftler qualitative Veränderungsprozesse, zu deren Analyse sie meist statische Untersuchungsmethoden verwenden, die die zeitliche Dimension des Problems vernachlässigen. Aus diesem Grunde legt der Autor eine Einführung in die dynamische Analyse von diskreten abhängigen Variablen vor. Er will diese Methode bei Sozialwissenschaftlern bekanntmachen und aufzeigen, warum ihre Anwendung von Bedeutung ist und wie sie interpretiert werden kann. Die dynamischen Modelle beschreiben mathematisch den Prozeß des Wechsels einer sozialen Einheit (Personen, Staaten) von einem qualitativen Status (z. B. Person: verheiratet, nicht verheiratet) in einen anderen. Dies wird an verschiedenen Modellen exemplarisch deutlich gemacht. Im Anhang ist weiterführende Literatur zur vertiefenden Beschäftigung aufgeführt. (AG

Dynamics of Formal Political Structure: An Event-History Analysis

The authors apply event history analysis to records on 90 countries from 1950-1975 to test hypotheses consistent with world systems and modernization hypotheses. The hypotheses predict factors associated with political change from/to one-party and multi-party governments. Modernization hypotheses predict that changes making a society more modern (that is, more like European societies) increase the chances for multi-party democratic governments. World systems hypotheses predict that governments are more affected by a country’s place in the world economic system than by internal changes. Results here show small effects of modernizing on government form, and event history methods show a complex relationship between GNP per capita and form of government

Rapid consumption of phytoplankton and ice algae by Arctic soft-sediment benthic communities: Evidence using natural and 13C-labeled food materials

Reduction of sea ice in the Arctic may significantly alter the relative fluxes of phytoplankton and ice algae to the seafloor. To examine the response of Arctic benthic communities to changing food supplies, we incubated sediment cores collected from two sites (Smeerenburg Fjord, northwest Svalbard in May 2003 and Storfjord Trench, Barents Sea in May 2004) with controlled additions of natural phytoplankton and ice algal assemblages, and laboratory-cultured 13C-labeled ice algae (Nitzschia frigida, in 2004 only). We measured sediment respiration, pigments, lipid biomarkers, and compound-specific δ13C signals over the course of incubations. Both communities responded rapidly to the addition of food materials: regardless of food type, concentrations of organic biomarkers (pigments and fatty acids) decreased to the levels of control cores within seven days. Although we found no evidence for selective ingestion of the different food types by macrofauna, fatty acids were differentially consumed. The enriched polyunsaturated fatty acids of the ice algae were preferentially utilized compared to saturated and monounsaturated fatty acids bound in ice algae. However, the saturated and monounsaturated fatty acids of phytoplankton (with depleted polyunsaturated fatty acids) are utilized more efficiently than those counterparts bound in ice algae. Bacterial activity was stimulated by food addition, indicated by the immediate increase of bacteria-specific fatty acids, but the direct assimilation of 13C-labeled carbon into bacterial biomass was limited. Our results imply that Arctic benthic communities can meet their energetic requirements by altering strategies to assimilate different components from variable food supplies

Alternative Estimation Procedures for Event-History Analysis: A Monte Carlo Study

The authors compare alternative procedures for estimating parameters of event-history models: ordinary least squares, Kaplan-Meier least squares, maximum likelihood, and partial likelihood. They report results of simulations comparing maximum likelihood and partial likelihood estimators for small samples in terms of several potential sources of error. A related paper is Tuma, Hannan, and Groeneveld (1979)

Methodology issues concerning the accuracy of kinematic data collection and analysis using the ariel performance analysis system

Kinematics, the study of motion exclusive of the influences of mass and force, is one of the primary methods used for the analysis of human biomechanical systems as well as other types of mechanical systems. The Anthropometry and Biomechanics Laboratory (ABL) in the Crew Interface Analysis section of the Man-Systems Division performs both human body kinematics as well as mechanical system kinematics using the Ariel Performance Analysis System (APAS). The APAS supports both analysis of analog signals (e.g. force plate data collection) as well as digitization and analysis of video data. The current evaluations address several methodology issues concerning the accuracy of the kinematic data collection and analysis used in the ABL. This document describes a series of evaluations performed to gain quantitative data pertaining to position and constant angular velocity movements under several operating conditions. Two-dimensional as well as three-dimensional data collection and analyses were completed in a controlled laboratory environment using typical hardware setups. In addition, an evaluation was performed to evaluate the accuracy impact due to a single axis camera offset. Segment length and positional data exhibited errors within 3 percent when using three-dimensional analysis and yielded errors within 8 percent through two-dimensional analysis (Direct Linear Software). Peak angular velocities displayed errors within 6 percent through three-dimensional analyses and exhibited errors of 12 percent when using two-dimensional analysis (Direct Linear Software). The specific results from this series of evaluations and their impacts on the methodology issues of kinematic data collection and analyses are presented in detail. The accuracy levels observed in these evaluations are also presented

Benthic community response to ice algae and phytoplankton in Ny Ålesund, Svalbard

Author Posting. © Inter-Research, 2006. This article is posted here by permission of Inter-Research for personal use, not for redistribution. The definitive version was published in Marine Ecology Progress Series 310 (2006): 1-14, doi:10.3354/meps310001.We assessed the digestibility and utilization of ice algae and phytoplankton by the shallow, subtidal benthos in Ny Ålesund (Kongsfjord) on Svalbard (79°N, 12°E) using chlorophyll a (chl a), essential fatty acids (EFAs) and stable isotopes as tracers of food consumption and assimilation. Intact benthic communities in sediment cores and individuals of dominant benthic taxa were given ice algae, phytoplankton, 13C-enriched ice algae or a no food addition control for 19 to 32 d. Ice algae and phytoplankton had significantly different isotopic signatures and relative concentrations of fatty acids. In the food addition cores, sediment concentrations of chl a and the EFA C20:5(n-3) were elevated by 80 and 93%, respectively, compared to the control after 12 h, but decreased to background levels by 19 d, suggesting that both ice algae and phytoplankton were rapidly consumed. Whole core respiration rates in the ice algae treatments were 1.4 times greater than in the other treatments within 12 h of food addition. In the ice algae treatment, both suspension and deposit feeding taxa from 3 different phyla (Mollusca, Annelida and Sipuncula) exhibited significant enrichment in δ13C values compared to the control. Deposit feeders (15% uptake), however, exhibited significantly greater uptake of the 13C-enriched ice algae tracer than suspension feeders (3% uptake). Our study demonstrates that ice algae are readily consumed and assimilated by the Arctic benthos, and may be preferentially selected by some benthic species (i.e. deposit feeders) due to their elevated EFA content, thus serving as an important component of the Arctic benthic food web.Funding for this study came from the National Science Foundation (Grant numbers OPP- 0514115 to W.G.A.; OPP-0222410 to L.M.C.; OPP-0222408 to M.-Y.S.; OPP0222500 to G.R.L.), the Norwegian Research Council (Grant number 151815-720 to M.L.C.), the Howard Hughes Medical Institute through Bates College and the Maine Marine Research Fund

A New Approach to Age-Period-Cohort Analysis Using Partial Least Squares Regression: The Trend in Blood Pressure in the Glasgow Alumni Cohort

Due to a problem of identification, how to estimate the distinct effects of age, time period and cohort has been a controversial issue in the analysis of trends in health outcomes in epidemiology. In this study, we propose a novel approach, partial least squares (PLS) analysis, to separate the effects of age, period, and cohort. Our example for illustration is taken from the Glasgow Alumni cohort. A total of 15,322 students (11,755 men and 3,567 women) received medical screening at the Glasgow University between 1948 and 1968. The aim is to investigate the secular trends in blood pressure over 1925 and 1950 while taking into account the year of examination and age at examination. We excluded students born before 1925 or aged over 25 years at examination and those with missing values in confounders from the analyses, resulting in 12,546 and 12,516 students for analysis of systolic and diastolic blood pressure, respectively. PLS analysis shows that both systolic and diastolic blood pressure increased with students' age, and students born later had on average lower blood pressure (SBP: −0.17 mmHg/per year [95% confidence intervals: −0.19 to −0.15] for men and −0.25 [−0.28 to −0.22] for women; DBP: −0.14 [−0.15 to −0.13] for men; −0.09 [−0.11 to −0.07] for women). PLS also shows a decreasing trend in blood pressure over the examination period. As identification is not a problem for PLS, it provides a flexible modelling strategy for age-period-cohort analysis. More emphasis is then required to clarify the substantive and conceptual issues surrounding the definitions and interpretations of age, period and cohort effects

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types