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Author: Bollen, Kenneth A.
Resulting in 5 citations.
1. Bianconcini, Silvia
Bollen, Kenneth A.
The Latent Variable-Autoregressive Latent Trajectory Model: A General Framework for Longitudinal Data Analysis
Structural Equation Modeling: A Multidisciplinary Journal 25 (2018): 791-808.
Also: https://www.tandfonline.com/doi/abs/10.1080/10705511.2018.1426467
Cohort(s): NLSY79
Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis
Keyword(s): Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; Statistical Analysis; Unions; Wages

Permission to reprint the abstract has been denied by the publisher.

Bibliography Citation
Bianconcini, Silvia and Kenneth A. Bollen. "The Latent Variable-Autoregressive Latent Trajectory Model: A General Framework for Longitudinal Data Analysis." Structural Equation Modeling: A Multidisciplinary Journal 25 (2018): 791-808.
2. Biesanz, Jeremy C.
Deeb-Sossa, Natalia
Papadakis, Alison A.
Bollen, Kenneth A.
Curran, Patrick J.
The Role of Coding Time in Estimating and Interpreting Growth Curve Models
Psychological Methods 9,1 (March 2004): 30-52.
Also: http://psycnet.apa.org/journals/met/9/1/30/
Cohort(s): Children of the NLSY79
Publisher: American Psychological Association (APA)
Keyword(s): Modeling, Growth Curve/Latent Trajectory Analysis; Weight

The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of recoding time in growth curve models. Linear and quadratic growth model examples are provided, and the interpretation of estimates given a particular coding of time is illustrated. How and why the precision and statistical power of predictors of lower order growth curve components changes over time is illustrated and discussed. Recommendations include coding time to produce readily interpretable estimates and graphing lower order effects across time with appropriate confidence intervals to help illustrate and understand the growth process.
Bibliography Citation
Biesanz, Jeremy C., Natalia Deeb-Sossa, Alison A. Papadakis, Kenneth A. Bollen and Patrick J. Curran. "The Role of Coding Time in Estimating and Interpreting Growth Curve Models." Psychological Methods 9,1 (March 2004): 30-52.
3. Bollen, Kenneth A.
Brand, Jennie E.
A General Panel Model with Random and Fixed Effects: A Structural Equations Approach
Social Forces 89,1 (September 2010): 1-34.
Also: http://sf.oxfordjournals.org/content/89/1/1.abstract
Cohort(s): NLSY79
Publisher: Oxford University Press
Keyword(s): Discrimination; Fertility; Income; Modeling, Fixed Effects; Modeling, Random Effects; Mothers, Income; Wage Penalty/Career Penalty

Permission to reprint the abstract has not been received from the publisher.

Fixed and random effects models for longitudinal data are common in sociology. Their primary advantage is that they control for time-invariant omitted variables. However, analysts face several issues when they employ these models. One is the uncertainty of whether to apply the fixed effects (FEM) versus the random effects (REM) models. Another less discussed issue is that the FEM and REM models as usually implemented might be insufficiently flexible. For instance, the effects of variables, including the latent time-invariant variable, might change over time rather than be constant as in the usual FEM and REM. The latent time-invariant variable might correlate with some variables and not others. Lagged endogenous variables might be necessary. Alternatives that move beyond the classic FEM and REM models are known, but they involve different estimators and software that make these extended models difficult to implement and to compare. This paper presents a general panel model that includes the standard FEM and REM as special cases. In addition, it provides a sequence of nested models that provide a richer range of models that researchers can easily compare with likelihood ratio tests and fit statistics. Furthermore, researchers can implement our general panel model and its special cases in widely available structural equation models (SEMs) software.
Bibliography Citation
Bollen, Kenneth A. and Jennie E. Brand. "A General Panel Model with Random and Fixed Effects: A Structural Equations Approach." Social Forces 89,1 (September 2010): 1-34.
4. Bollen, Kenneth A.
Curran, Patrick J.
Latent Curve Models: A Structural Equation Perspective
Hoboken NJ: John Wiley & Sons, 2006.
Also: http://ebooks.ebookmall.com/ebook/214878-ebook.htm
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Wiley Online
Keyword(s): Behavior Problems Index (BPI); Family Income; Growth Curves; Modeling, Growth Curve/Latent Trajectory Analysis; Neighborhood Effects; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Weight

Permission to reprint the abstract has not been received from the publisher.

This volume is an eBook: http://ebooks.ebookmall.com/ebook/214878-ebook.htm.

This volume represents a comprehensive treatment of a model sometimes referred to as latent curve or growth curve models. Latent Curve Models analyzes LTMs from the perspective of structural equation modeling (SEM) with latent variables. Although the authors discuss simple regression-based procedures that are helpful in the early stages of LTM, most of the presentation will use SEMs as a driving tool throughout the text.

Bibliography Citation
Bollen, Kenneth A. and Patrick J. Curran. Latent Curve Models: A Structural Equation Perspective. Hoboken NJ: John Wiley & Sons, 2006..
5. Curran, Patrick J.
Bollen, Kenneth A.
Best of Both Worlds: Combining Autoregressive and Latent Curve Models
In: New Methods of the Analysis of Change. A. G. Sayer, ed. Washington, DC: American Psychological Association, 2001: pp. 107-135
Cohort(s): Children of the NLSY79
Publisher: American Psychological Association (APA)
Keyword(s): Behavior Problems Index (BPI); Depression (see also CESD); Markov chain / Markov model; Modeling, Fixed Effects; Modeling, Growth Curve/Latent Trajectory Analysis; Statistical Analysis

Discusses the autoregressive model (or "fixed effects Markov simplex model") and random coefficient growth curve models as being two analytic approaches to the theoretical conceptualization and statistical analysis of panel data. An extended empirical example is presented in order to illustrate the authors' ongoing efforts to synthesize these two models. They begin with a description of a theoretical substantive question that motivates the development of the synthesized model, they then present a review of the univariate and bivariate autoregressive simplex models followed by a general description of the univariate and bivariate latent curve models. The synthesis of the simplex and latent curve models is proposed for both the univariate and bivariate cases, and these are applied to the empirical data set to evaluate a series of questions relating to the developmental relation between antisocial behavior and depressive symptomatology.
Bibliography Citation
Curran, Patrick J. and Kenneth A. Bollen. "Best of Both Worlds: Combining Autoregressive and Latent Curve Models" In: New Methods of the Analysis of Change. A. G. Sayer, ed. Washington, DC: American Psychological Association, 2001: pp. 107-135