Statistical methods in longitudinal research. Alexander von Eye (Ed), Academic Press, Boston, 1990. VOL. 1: Principles and structuring change. No. of pages: xiv + 256. Price: $34.95, ISBN: 0-12-724962-1 VOL. 2: Time series and categorical longitudinal data. NO. of pages: xiv + 312. Price: $34.95, ISBN: 0-12-724963-X

1992 ◽  
Vol 11 (7) ◽  
pp. 979-980
Author(s):  
Andrew Pickles
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Statistical Methods ◽  
Longitudinal Research ◽  
Academic Press

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

Lag as moderator meta-analysis: A methodological approach for synthesizing longitudinal data

2018 ◽  
Vol 43 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Noel A. Card
Keyword(s):  
Longitudinal Data ◽  
Human Development ◽  
Longitudinal Research ◽  
Meta Analysis ◽  
Methodological Approach ◽  
Mixed Effects ◽  
Effect Sizes ◽  
Time Lags ◽  
Research Findings ◽  
The Impact

Longitudinal data are common and essential to understanding human development. This paper introduces an approach to synthesizing longitudinal research findings called lag as moderator meta-analysis (LAMMA). This approach capitalizes on between-study variability in time lags studied in order to identify the impact of lag on estimates of stability and longitudinal prediction. The paper introduces linear, nonlinear, and mixed-effects approaches to LAMMA, and presents an illustrative example (with syntax and annotated output available as online Supplementary Materials). Several extensions of the basic LAMMA are considered, including artifact correction, multiple effect sizes from studies, and incorporating age as a predictor. It is hoped that LAMMA provides a framework for synthesizing longitudinal data to promote greater accumulation of knowledge in developmental science.

Connecting theory to methods in longitudinal research

2021 ◽  
Author(s):  
Christopher James Hopwood ◽  
Wiebke Bleidorn ◽  
Aidan G.C. Wright
Keyword(s):  
Data Collection ◽  
Longitudinal Data ◽  
Longitudinal Research ◽  
Psychological Processes ◽  
Data Collection And Analysis ◽  
Longitudinal Data Collection

Advances in methods for longitudinal data collection and analysis have prompted a surge of research on psychological processes. However, decisions about how to time assessments are often not tethered explicitly to theories about psychological processes, but are instead justified on methodological (e.g., power) or practical (e.g., feasibility) grounds. In many cases, methodological decisions are not explicitly justified at all. The disconnect between theories about processes and the timing of assessments in longitudinal research has contributed to mis-specified models, interpretive errors, mixed findings, and non-specific conclusions. In this paper, we argue that higher demands should be placed on researchers to connect theories to methods in longitudinal research. We review instances of this disconnection and offer potential solutions as they pertain to four general questions for longitudinal researchers: how should time be scaled, how many assessments are needed, how frequently should assessments occur, and when should assessments happen?

Detecting Determinism in Human Posture Control Data

1998 ◽  
Vol 08 (01) ◽  
pp. 179-188 ◽  
Author(s):  
L. Y. Cao ◽  
B. G. Kim ◽  
J. Kurths ◽  
S. Kim
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Nonstationary Time Series ◽  
Posture Control ◽  
Nonlinear Prediction ◽  
Control Data ◽  
Human Posture ◽  
Prediction Techniques

In this paper, determinism in human posture control data is investigated using the approach of nonlinear prediction. We first comment that one should be cautious of using some statistical methods to analyze nonstationary time series. Then we test the predictability of the human posture control data with different prediction techniques, and investigate how nonstationarity and noise affect the prediction results. Different time series are tested, including data from healthy and ill persons, and different predictabilities are found in different time series.

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