The application of time series methods to moderate span longitudinal data.

1991 ◽  
pp. 75-87 ◽  
Author(s):  
Kenneth Jones
Keyword(s):  
Time Series ◽  
Longitudinal Data

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.

How to Perform Idiographic and a Combination of Idiographic and Nomothetic Approaches

2010 ◽  
Vol 218 (3) ◽  
pp. 166-174 ◽  
Author(s):  
Michaela Schmidt ◽  
Franziska Perels ◽  
Bernhard Schmitz
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Hierarchical Linear Modeling ◽  
Self Regulation ◽  
Training Group ◽  
Group Level ◽  
Linear Modeling ◽  
Individual Level ◽  
Cross Correlations ◽  
Time Series Analyses

The aim of the study is to combine and compare person-oriented and nomothetic approaches to analyze longitudinal data with time series analyses and hierarchical linear modeling (HLM). Based on the evaluation of an intervention study both approaches were used to compare individual and group data. In this study, a training was implemented to foster students’ self-regulation and selected results were presented at the individual and group level for the variables planning and motivation. To analyze data with time series analysis, cross-correlations and trend analyses were conducted. Cross-correlations revealed similar results on the aggregated and individual level whereas trend analysis indicated different results of these two levels. Results of HLM analyses for longitudinal data suggested that students’ motivation has more influence than the type of training group on students’ planning. The findings demonstrate that individual and group-level results differ and that both methods have different focuses. This means that it is useful to combine time series analyses and HLM approaches when analyzing longitudinal data.

No Time Like the Present

2017 ◽  
Vol 26 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Ellen L. Hamaker ◽  
Marieke Wichers
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Multilevel Modeling ◽  
Strong Increase ◽  
Single Individual ◽  
Psychological Processes ◽  
Daily Diaries ◽  
Intensive Longitudinal Data ◽  
Level 1 ◽  
Level 2

There has been a strong increase in the number of studies based on intensive longitudinal data, such as those obtained with experience sampling and daily diaries. These data contain a wealth of information regarding the dynamics of processes as they unfold within individuals over time. In this article, we discuss how combining intensive longitudinal data with either time-series analysis, which consists of modeling the temporal dependencies in the data for a single individual, or dynamic multilevel modeling, which consists of using a time-series model at Level 1 to describe the within-person process while allowing for individual differences in the parameters of these processes at Level 2, has led to new insights in clinical psychology. In addition, we discuss several methodological and statistical challenges that researchers face when they are interested in studying the dynamics of psychological processes using intensive longitudinal data.

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