A primer in longitudinal data analysis /
Toon Taris' survival guide takes the reader through the strengths and weaknesses of longitudinal research, making clear how to design a longitudinal study, how to collect data most effectively and how to interpret results.
| Основен автор: | Taris, Toon. |
|---|---|
| Формат: | Електронна книга |
| Език: | English |
| Публикувано: |
London :
Sage,
2000.
|
| Предмети: | |
| Онлайн достъп: |
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=251795 |
| Подобни документи: |
Print version::
Primer in longitudinal data analysis. |
Съдържание:
- Longitudinal data and longitudinal designs
- Covariation and causation
- Designs for collecting longitudinal data
- Nonresponse in longitudinal research
- Nonresponse in cross-sectional and longitudinal designs
- Minimizing nonresponse and attrition
- Detecting selective nonresponse
- Measuring concepts across time: issues of stability and meaning
- Types of change and stability
- Exploratory vs. confirmatory factor analysis
- Using the confirmatory factor-analytic model to assess structural invariance
- Issues in discrete-time panel analysis
- Measuring change in discrete time
- Dependence on initial values: the sophomore slump
- Change scores: what is the difference?
- The regressor variable approach and the return of the difference score
- Assessing causal direction across time: cross-lagged panel analysis
- Analysis of repeated measures
- Examining across-time growth
- Analysis of variance: some basics
- Analysis of variance for longitudinal survey data
- Analyzing durations
- Survival-, failure time- and event history-analysis
- Survival data
- Continuous-time survival analysis: hazard function and survival function
- Analysis of covariates: the stratification approach
- Parametric and semi-parametric approaches to analyzing covariates
- Discrete-time survival analysis
- Analyzing sequences
- Event- vs. career-centered modes of analysis
- Measuring career change: characterizing development
- Creating classifications of careers: distance-based methods
- Same-order methods: sequencing careers.