Number of Subjects and Time Points Needed for Multilevel Time-Series Analysis: A Simulation Study of Dynamic Structural Equation Modeling

2017 ◽  
Vol 25 (4) ◽  
pp. 495-515 ◽  
Author(s):  
Mårten Schultzberg ◽  
Bengt Muthén
Keyword(s):  
Time Series ◽  
Structural Equation Modeling ◽  
Time Series Analysis ◽  
Simulation Study ◽  
Structural Equation ◽  
Equation Modeling ◽  
Time Points ◽  
Series Analysis

scholarly journals Measurement error in time-series analysis: a simulation study comparing modelled and monitored data

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Barbara K Butland ◽  
Ben Armstrong ◽  
Richard W Atkinson ◽  
Paul Wilkinson ◽  
Mathew R Heal ◽  
...  
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Series Analysis ◽  
Simulation Study ◽  
Series Analysis

scholarly journals Reliability for Tests With Items Having Different Numbers of Ordered Categories

2019 ◽  
Vol 44 (2) ◽  
pp. 137-149
Author(s):  
Seohyun Kim ◽  
Zhenqiu Lu ◽  
Allan S. Cohen
Keyword(s):  
Structural Equation Modeling ◽  
Simulation Study ◽  
Test Scores ◽  
Structural Equation ◽  
Reliability Coefficient ◽  
Coefficient Alpha ◽  
Equation Modeling ◽  
Ordered Categories

This study describes a structural equation modeling (SEM) approach to reliability for tests with items having different numbers of ordered categories. A simulation study is provided to compare the performance of this reliability coefficient, coefficient alpha and population reliability for tests having items with different numbers of ordered categories, a one-factor and a bifactor structures, and different skewness distributions of test scores. Results indicated that the proposed reliability coefficient was close to the population reliability in most conditions. An empirical example was used to illustrate the performance of the different coefficients for a test of items with two or three ordered categories.

Introduction to Analysis Using Time Components

2010 ◽  
pp. 154-192
Author(s):  
Patricia Cerrito ◽  
John Cerrito
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fixed Time ◽  
Analysis Time ◽  
Time Intervals ◽  
Time Points ◽  
Series Analysis ◽  
Time Component ◽  
Time Stamps ◽  
Insurance Data

The introduction of a time component requires the use of statistical methods that can utilize dependent data. The assumption of independence that is required for regression models is no longer applicable. In this section, we will work with time series analysis. Time series analysis requires that data are collected at discrete, fixed time intervals. Observational and insurance data contain time stamps as to the date of service. These time stamps are transactional in nature and do not occur at fixed time intervals. Therefore, the first step in such an analysis is to convert the transactional time points into fixed time intervals. We need to decide upon the interval: every minute, hour, day, week, month, year. The specific interval will depend upon the analysis to be performed. Once that is completed, the standard time series analysis methods can be used. As an example, we use the MEPS dataset for medications. We use the date of January 1 as time zero.

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