scholarly journals Cross-Lagged Panel Model in Medical Research: A Cautionary Note

2018 ◽  
Author(s):  
Satoshi Usami ◽  
Naoya Todo ◽  
Kou Murayama
Keyword(s):  
Medical Research ◽  
Case Studies ◽  
Model Fit ◽  
Parameter Estimates ◽  
Reciprocal Effects ◽  
Simulation Studies ◽  
Longitudinal Models ◽  
Alternative Models ◽  
Panel Model ◽  
Potential Alternative

Longitudinal designs provide a strong inferential basis for uncovering reciprocal effects or causality between variables. For this analytic purpose, a cross-lagged panel model (CLPM) has been widely used in medical research, but the use of the CLPM has recently been criticized in methodological literature because parameter estimates in the CLPM conflate between-person and within-person processes. The aim of this study is to present some alternative models of the CLPM that can be used to examine reciprocal effects, and to illustrate potential consequences of ignoring the issue. A literature search, case studies, and simulation studies are used for this. We examined more than 300 medical papers published since 2009 that applied cross-lagged longitudinal models, finding that in all studies only a single model (typically, the CLPM) was performed and potential alternative models were not considered to test reciprocal effects. In 49% of the studies, only two time points were used, which makes it impossible to test such alternative models. Case studies and simulation studies showed that the CLPM often has worse model fit and markedly different estimates of cross-lagged parameters than alternative models, suggesting that research that relies on the CLPM only may draw erroneous conclusions regarding the presence, predominance, and sign of reciprocal effects as well as about causality.

Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

1999 ◽  
Vol 15 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Lutz F. Hornke
Keyword(s):  
Measurement Error ◽  
Computerized Adaptive Testing ◽  
Test Procedure ◽  
Adaptive Testing ◽  
Parameter Estimates ◽  
Simulation Studies ◽  
Computerized Adaptive Test ◽  
Item Banks ◽  
Item Parameters ◽  
General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

On assessing model fit for distribution-free longitudinal models under missing data

2013 ◽  
Vol 33 (1) ◽  
pp. 143-157 ◽  
Author(s):  
P. Wu ◽  
X.M. Tu ◽  
J. Kowalski
Keyword(s):  
Missing Data ◽  
Model Fit ◽  
Longitudinal Models ◽  
Distribution Free

scholarly journals Compensating Qualitative Rating Distortion of User Experience Evaluation Based on Prospect Theory

2019 ◽  
Vol 11 (23) ◽  
pp. 6815
Author(s):  
Min Chul Lee ◽  
Jaehyun Park
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Case Studies ◽  
User Experience ◽  
Cognitive Distortion ◽  
Model Fit ◽  
Online Questionnaire ◽  
Measurement Results ◽  
Preliminary Study ◽  
User Value

Psychophysical assessment may be affected by cognitive distortion. Although the theory was originally developed to revise decision making in uncertain situations, prospect theory can be applied to psychophysical measurements, which was verified in a previous preliminary study. Two case studies were used to validate the utilization of prospect theory in psychophysical measurements. Affective satisfaction dimensions were rated by participants for an experimental device using a 0–100 scale. Performance of affective satisfaction models increased with the application of prospect theory-based compensation. Hundreds of participants evaluated the user value of their own devices via an online questionnaire. Although model fit performance increased slightly with transformed data, more case studies are needed to investigate the utility of prospect theory on user value or on a range of target constructs. The application of prospect theory in various situations of psychophysical measurement can be expected to improve and compensate for measurement results.

Calibration of three resuspension/sedimentation models

1998 ◽  
Vol 37 (3) ◽  
pp. 41-49 ◽  
Author(s):  
Gerard Blom ◽  
R. Hans Aalderink
Keyword(s):  
Suspended Solids ◽  
Statistical Evaluation ◽  
Model Fit ◽  
Parameter Estimates ◽  
Flume Experiments ◽  
Parameter Estimations ◽  
In Situ Observations ◽  
Squared Residuals ◽  
Very High

Three resuspension and sedimentation models (Blom, Lick and Partheniades and Krone) are calibrated and evaluated on data from flume experiments with sediments from Lake Ketel and in situ suspended solids measurements. We applied a formal parameter estimation technique in combination with a statistical evaluation of the model fit and parameter estimates. All three models produce a reasonable reconstruction of the data from the flume experiment and the in situ observations. The differences in the model fit of the three models are small, except for the in situ observations. Here the sum of squared residuals for Partheniades and Krone's is about twice the sum for Blom's and Lick's model. The correlation between parameters in resuspension/sedimentation models can be very high, leading to an uncertainty in parameter estimates of 25-50. The parameter estimations based on the flume data are up to orders of magnitude higher than those estimated from field observations.

scholarly journals Inferring the Demographic History of Inbred Species from Genome-Wide SNP Frequency Data

2020 ◽  
Vol 37 (7) ◽  
pp. 2124-2136
Author(s):  
Paul D Blischak ◽  
Michael S Barker ◽  
Ryan N Gutenkunst
Keyword(s):  
Puma Concolor ◽  
Demographic History ◽  
Random Mating ◽  
Simulated Data ◽  
Conservation Agriculture ◽  
Model Fit ◽  
Parameter Estimates ◽  
History Of ◽  
Demographic Inference ◽  
Inbred Populations

Abstract Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model’s ability to coestimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleracea var. capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately coestimate demographic parameters and inbreeding even for highly inbred populations (F = 0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species.

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