## Longitudinal data analysis: designs, models and methodsBy looking at the processes of change over time - by carrying out longitudinal studies - researchers answer questions about learning, development, educational growth, social change and medical outcomes. However, longitudinal research has many faces. This book examines all the main approaches as well as newer developments (such as structural equation modelling, multilevel modelling and optimal scaling) to enable the reader to gain a thorough understanding of the approach and make appropriate decisions about which technique can be applied to the research problem. Conceptual explanations are used to keep technical terms to a minimum; examples are provided for each approach; issues of design, measurement and significance are considered; and a standard notation is used throughout. |

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### Contents

Box Proposed by Cattell 1946 1952 1988 | 6 |

Analysis of longitudinal categorical data using optimal | 46 |

of Multiple Nominal Variables and Single Category | 72 |

Copyright | |

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### Common terms and phrases

assumption autoregressive behaviour between-subject Bijleveld canonical analysis categorical time variable categorical variables category quantifications Chapter component loadings correlation covariance cross-sectional data box data matrix degrees of freedom dependent depression difference scores dimension discussed effects eigenvalues equal error estimates example Figure goodness-of-fit growth curve model iables indicators input variables instance intercept interpretation investigate lag(l lagged latent class model latent Markov model latent variables level-2 linear dynamic system log-linear analysis log-linear models longitudinal data longitudinal research manifest variables MANOVA means missing data mixed Markov model multilevel analysis multilevel models multiple correspondence analysis multivariate nonlinear principal components null hypothesis number of subjects object scores observed variables occasions parameters predicted principal components analysis problem random relations repeated measures sample Section solution specified standardized residuals statistical status structural equation modelling sums of squares Table tion transition matrices univariate variance variance-covariance matrix variation vector versions within-subject zero