Introduction 1. Woman's Place in Man's Life Cycle 2. Images of Relationship 3. Concepts of Self and Morality 4. Crisis and Transition 5. Women's Rights and Women's Judgment 6. Visions of Maturity References Index of Study Participants General Index

In a Different Voice. Psychological Theory and Women’s Development. Cambridge, MA (Harvard University Press) 1982.

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Economics and Information Theory

C4.5: Programs for Machine Learning (書評)

This article describes the many capabilities offered by the TraMineR toolbox for categorical sequence data. It focuses more specifically on the analysis and rendering of state sequences. Addressed features include the description of sets of sequences by means of transversal aggregated views, the computation of longitudinal characteristics of individual sequences and the measure of pairwise dissimilarities. Special emphasis is put on the multiple ways of visualizing sequences. The core element of the package is the state se- quence object in which we store the set of sequences together with attributes such as the alphabet, state labels and the color palette. The functions can then easily retrieve this information to ensure presentation homogeneity across all printed and graphical displays. The article also demonstrates how TraMineR’s outcomes give access to advanced analyses such as clustering and statistical modeling of sequence data.

/pdf/analyzing-and-visualizing-state-sequences-in-r-with-traminer-42gncygwun.pdf

Analyzing and Visualizing State Sequences in R with TraMineR

This is a comparative study of the multiple ways of measuring dissimilarities between state sequences. The originality of the study is the focus put on the differences between sequences that are sociologically important when studying life courses such as family life trajectories or professional careers. These differences essentially concern the sequencing (the order in which successive states appear), the timing and the duration of the spells in successive states. The study examines the sensitivity of the measures to these three aspects analytically and empirically by means of simulations. Even if some distance measures underperform, the study shows that there is no universally optimal distance index, and that the choice of a measure depends on which aspect we want to focus on. From the review and simulation results, the paper derives guidelines to help the end user to choose the right dissimilarity measure for her or his research objectives. This study also introduces novel ways of measuring dissimilarities that overcome some flaws in existing measures.

/pdf/what-matters-in-differences-between-life-trajectories-a-3gpppm0ru5.pdf

What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures

The tree-based TVCM algorithm and its implementation in the R package vcrpart are introduced for generalized linear models. The purpose of TVCM is to learn whether and how the coefficients of a regression model vary by moderating variables. A separate partition is built for each potentially varying coefficient, allowing the user to specify coefficient-specific sets of potential moderators, and allowing the algorithm to select moderators individually by coefficient. In addition to describing the algorithm, the TVCM is evaluated using a benchmark comparison and a simulation study and the R commands are demonstrated by means of empirical applications.

/pdf/coefficient-wise-tree-based-varying-coefficient-regression-174a1a8mig.pdf

Coefficient-wise tree-based varying coefficient regression with vcrpart

http://mephisto.unige.ch/pub/publications/gr/WidmerRitschard-online-ALCR09_preprint.pdf

The De-Standardization of the Life Course: Are Men and Women Equal?

In this article we define a methodological framework for analyzing the relationship between state sequences and covariates. Inspired by the ANOVA principles, our approach looks at how the covariates explain the discrepancy of the sequences. We use the pairwise dissimilarities between sequences to determine the discrepancy which makes it then possible to develop a series of statistical-significancebased analysis tools. We introduce generalized simple and multi-factor discrepancy-based methods to test for dierences between groups, a pseudo R 2 for measuring the strength of sequence‐covariate associations, a generalized Levene statistic for testing dierences in the within-group discrepancies, as well as tools and plots for studying the evolution of the dierences along the timeframe and a regression tree method for discovering the most significant discriminant covariates and their interactions. In addition, we extend all methods to account for case weights. The scope of the proposed methodological framework is illustrated using a real-world sequence dataset.

/pdf/discrepancy-analysis-of-state-sequences-1duhftaeg5.pdf

Gilbert Ritschard

Papers

Analyzing and Visualizing State Sequences in R with TraMineR

What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures

Coefficient-wise tree-based varying coefficient regression with vcrpart

The De-Standardization of the Life Course: Are Men and Women Equal?

Discrepancy Analysis of State Sequences