Predicting Network Events to Assess Goodness of Fit of Relational Event Models

2019 ◽  
Vol 27 (4) ◽  
pp. 556-571 ◽  
Author(s):  
Laurence Brandenberger
Keyword(s):  
Goodness Of Fit ◽  
Temporal Dynamics ◽  
Simple Procedure ◽  
Model Fit ◽  
Network Characteristics ◽  
Event Sequences ◽  
Simulated Event ◽  
Unbiased Estimates ◽  
Event Models ◽  
Biased Estimates

Relational event models are becoming increasingly popular in modeling temporal dynamics of social networks. Due to their nature of combining survival analysis with network model terms, standard methods of assessing model fit are not suitable to determine if the models are specified sufficiently to prevent biased estimates. This paper tackles this problem by presenting a simple procedure for model-based simulations of relational events. Predictions are made based on survival probabilities and can be used to simulate new event sequences. Comparing these simulated event sequences to the original event sequence allows for in depth model comparisons (including parameter as well as model specifications) and testing of whether the model can replicate network characteristics sufficiently to allow for unbiased estimates.

scholarly journals Design and Analysis of Experiments in Networks: Reducing Bias from Interference

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Dean Eckles ◽  
Brian Karrer ◽  
Johan Ugander
Keyword(s):  
Sufficient Conditions ◽  
Bias Reduction ◽  
Experimental Unit ◽  
Randomized Experiments ◽  
Cluster Randomization ◽  
Graph Cluster ◽  
Unbiased Estimates ◽  
Bias Variance ◽  
Substantial Bias ◽  
Biased Estimates

AbstractEstimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and analysis methods assume the absence of this interference, and result in biased estimates of causal effects when it exists. While some assumptions can lead to unbiased estimates, these assumptions are generally unrealistic in the context of a network and often amount to assuming away the interference. In this work, we evaluate methods for designing and analyzing randomized experiments under minimal, realistic assumptions compatible with broad interference, where the aim is to reduce bias and possibly overall error in estimates of average effects of a global treatment. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference; these conditions also give lower bounds on treatment effects. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias reductions and, despite a bias–variance tradeoff, error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.

scholarly journals A Revised Model of Trust in Internet-Based Health Information and Advice: Cross-Sectional Questionnaire Study

10.2196/11125 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e11125
Author(s):  
Elizabeth Sillence ◽  
John Matthew Blythe ◽  
Pam Briggs ◽  
Mark Moss
Keyword(s):  
Goodness Of Fit ◽  
Online Survey ◽  
Patient Experiences ◽  
Model Fit ◽  
Key Factors ◽  
The United Kingdom ◽  
Personal Experiences ◽  
And Coping ◽  
Fit Index ◽  
Significant Direct Effect

Background The internet continues to offer new forms of support for health decision making. Government, charity, and commercial websites increasingly offer a platform for shared personal health experiences, and these are just some of the opportunities that have arisen in a largely unregulated arena. Understanding how people trust and act on this information has always been an important issue and remains so, particularly as the design practices of health websites continue to evolve and raise further concerns regarding their trustworthiness. Objective The aim of this study was to identify the key factors influencing US and UK citizens’ trust and intention to act on advice found on health websites and to understand the role of patient experiences. Methods A total of 1123 users took part in an online survey (625 from the United States and 498 from the United Kingdom). They were asked to recall their previous visit to a health website. The online survey consisted of an updated general Web trust questionnaire to account for personal experiences plus questions assessing key factors associated with trust in health websites (information corroboration and coping perception) and intention to act. We performed principal component analysis (PCA), then explored the relationship between the factor structure and outcomes by testing the fit to the sampled data using structural equation modeling (SEM). We also explored the model fit across US and UK populations. Results PCA of the general Web trust questionnaire revealed 4 trust factors: (1) personal experiences, (2) credibility and impartiality, (3) privacy, and (4) familiarity. In the final SEM model, trust was found to have a significant direct effect on intention to act (beta=.59; P<.001), and of the trust factors, only credibility and impartiality had a significant direct effect on trust (beta=.79; P<.001). The impact of personal experiences on trust was mediated through information corroboration (beta=.06; P=.04). Variables specific to electronic health (eHealth; information corroboration and coping) were found to substantially improve the model fit, and differences in information corroboration were found between US and UK samples. The final model accounting for all factors achieved a good fit (goodness-of-fit index [0.95], adjusted goodness-of-fit index [0.93], root mean square error of approximation [0.50], and comparative fit index [0.98]) and explained 65% of the variance in trust and 41% of the variance in intention to act. Conclusions Credibility and impartiality continue to be key predictors of trust in eHealth websites. Websites with patient experiences can positively influence trust but only if users first corroborate the information through other sources. The need for corroboration was weaker in the United Kingdom, where website familiarity reduced the need to check information elsewhere. These findings are discussed in relation to existing trust models, patient experiences, and health literacy.

scholarly journals State Variable Effects in Graphical Event Models

2020 ◽  
Author(s):  
Debarun Bhattacharjya ◽  
Dharmashankar Subramanian ◽  
Tian Gao
Keyword(s):  
Real World ◽  
Continuous Time ◽  
Point Processes ◽  
Temporal Dynamics ◽  
Search Algorithm ◽  
State Variables ◽  
Discrete State ◽  
State Variable ◽  
Event Models ◽  
Real World Datasets

Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.

A New Method for Evaluating Stochastic Models of Brand Choice

1970 ◽  
Vol 7 (3) ◽  
pp. 300-306 ◽  
Author(s):  
David A. Aaker
Keyword(s):  
Stochastic Models ◽  
Goodness Of Fit ◽  
Value Function ◽  
Choice Model ◽  
Brand Choice ◽  
Mean Value ◽  
Model Fit ◽  
New Method ◽  
Structural Comparison ◽  
Goodness Of Fit Test

This article explores the use of a brand choice stochastic model's mean value function in evaluating two models empirically, using a common set of purchase data. The linear learning model fit the data well, but its mean value function was not capable of making reasonable predictions of successive, aggregate purchasing statistics. Another brand choice model, the new trier model, was found to perform much better. The results suggest that model tests should not be restricted to the usual goodness-of-fit test, especially in situations of non-stationarity. A structural comparison of the two models focuses on their different approaches to nonstationarity.

scholarly journals How well does your model fit the data?

2001 ◽  
Vol 3 (1) ◽  
pp. 49-55 ◽  
Author(s):  
M. J. Hall
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Computer Modelling ◽  
Synthetic Data ◽  
Model Fit ◽  
Coefficient Of Determination ◽  
Input Output

Despite almost five decades of activity on the computer modelling of input–output relationships, little general agreement has emerged on appropriate indices for the goodness-of-fit of a model to a set of observations of the pertinent variables. The coefficient of efficiency, which is closely allied in form to the coefficient of determination, has been widely adopted in many data mining and modelling exercises. Values of this coefficient close to unity are taken as evidence of good matching between observed and computed flows. However, studies using synthetic data have demonstrated that negative values of the coefficient of efficiency can occur both in the presence of bias in computed outputs, and when the computed volume of flow greatly exceeds the observed volume of flow. In contrast, the coefficient of efficiency lacks discrimination for cases close to perfect reproduction. In the latter case, a coefficient based upon the first differences of the data proves to be more helpful.

scholarly journals Cutoff criteria for overall model fit indexes in generalized structured component analysis

2020 ◽  
Vol 8 (4) ◽  
pp. 189-202
Author(s):  
Gyeongcheol Cho ◽  
Heungsun Hwang ◽  
Marko Sarstedt ◽  
Christian M. Ringle
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Model Fit ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
Root Mean Square Residual ◽  
Fit Indexes ◽  
Cutoff Criteria

AbstractGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

scholarly journals Psychometric validation of the Italian version of the Reported and Intended Behaviour Scale (RIBS)

2015 ◽  
Vol 25 (5) ◽  
pp. 485-492 ◽  
Author(s):  
L. Pingani ◽  
S. Evans-Lacko ◽  
M. Luciano ◽  
V. Del Vecchio ◽  
S. Ferrari ◽  
...  
Keyword(s):  
Mental Health ◽  
General Population ◽  
Mental Health Professionals ◽  
Goodness Of Fit ◽  
Mental Health Problems ◽  
Model Fit ◽  
Psychometric Validation ◽  
Validated Questionnaire ◽  
Fit Index ◽  
Intended Behaviour

Background.Many instruments have been developed and validated to assess the stigma associated with mental disorders and its various domains across different populations. To our knowledge, the Reported and Intended Behaviour Scale (RIBS) is the only validated questionnaire to analyse the presence of reported and intended stigmatising/discriminatory behaviours towards people with mental health problems in the general population. The aims of the study presented herein are to translate and validate the RIBS in Italian language and to adapt it to the Italian socio-cultural background (RIBS-I).Method.The RIBS considers reported and intended behaviours across four different domains: (1) living with, (2) working with, (3) living nearby and (4) continuing a relationship with someone with a mental health problem. The validation process included four phases: (1) translation/back translation of the questionnaire from English to Italian and vice versa; (2,3) face validity and reliability of RIBS-I; (4) description of model fit through confirmatory factor analysis. The questionnaire was administered to a sample of the general public via distribution in public places such as shopping centres, markets, squares, cinemas and other gathering places. Questionnaires were administered by trained mental health professionals.Results.A total of 447 lay respondents were recruited. The mean age was 38.08 (s.d. = ±14.74) years. Fifty-seven per cent of the sample (n = 257) were female. The Cronbach alpha of RIBS-I was 0.83. All indices of model fit were above the reference values: Goodness of Fit Index (GFI) = 0.987 (GFI > 0.9); Adjusted Goodness of Fit Index (AGFI) = 0.975 (AGFI > 0.9); Comparative Fit Index (CFI) = 0.994 (CFI > 0.9); and Root-Mean-Square Error of Approximation (RMSEA) = 0.023 (RMSEA < 0.05). The χ2 = 23.60 (df = 19; p = 0.21) and χ2/df = 1.24 supported the model.Conclusions.The RIBS-I demonstrated good psychometric properties and it can be considered a useful tool to: (1) assess stigmatising (actual or potential) behaviours in the general population; (2) test the efficacy of anti-stigma campaigns and actions; (3) design further studies to better understand the relationship between the three different components of stigmatisation: knowledge, attitudes and behaviours.

Doubly Robust Estimation with the R Package drgee

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Johan Zetterqvist ◽  
Arvid Sjölander
Keyword(s):  
Robust Estimation ◽  
Linear Models ◽  
Model Misspecification ◽  
R Package ◽  
Epidemiologic Research ◽  
Important Concern ◽  
Doubly Robust Estimation ◽  
Unbiased Estimates ◽  
Doubly Robust ◽  
Biased Estimates

AbstractA common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in generalized estimating equations (GEEs). If the covariates are high-dimensional, then it may be difficult to well specify the model. This is an important concern, since model misspecification may lead to biased estimates. Doubly robust estimation is an estimation technique that offers some protection against model misspecification. It utilizes two models, one for the outcome and one for the exposure, and produces unbiased estimates of the exposure-outcome association if either model is correct, not necessarily both. Despite its obvious appeal, doubly robust estimation is not used on a regular basis in applied epidemiologic research. One reason for this could be the lack of up-to-date software. In this paper we describe a new

scholarly journals Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study

2020 ◽  
Author(s):  
Simon L Turner ◽  
Andrew B Forbes ◽  
Amalia Karahalios ◽  
Monica Taljaard ◽  
Joanne E McKenzie
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Simulated Data ◽  
Population Level ◽  
Interrupted Time Series ◽  
Ordinary Least Squares ◽  
In Series ◽  
Time Series Studies ◽  
Unbiased Estimates ◽  
Biased Estimates

AbstractInterrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. To our knowledge, no studies have compared the performance of different statistical methods for this design. We simulated data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series.

scholarly journals PENGEMBANGAN INSTRUMEN SIKAP SPIRITUAL PADA SISWA SEKOLAH DASAR

2017 ◽  
Vol 8 (1) ◽  
pp. 17-25
Author(s):  
Muhammad Ichsan Muchtar
Keyword(s):  
Goodness Of Fit ◽  
Second Order ◽  
Model Fit ◽  
Factor I ◽  
Confirmatory Factor ◽  
Fit Index

Tujuan penelitian ini adalah untuk mengembangkan instrumen sikap spiritual pada siswa Sekolah Dasar (SD). Penelitian ini menggunakan metode SEM dengan second order confirmatory factor analyisis kepada 300 orang siswa SD dalam dua tahap, masing-masing terdiri dari 150 orang responden, untuk validasi konstruk secara empiris dan ketepatan model (model fit). Hasil analisis uji empiris menunjukkan ada 3 dimensi dan 12 indikator dengan loading factor λ≥ 0.30, t-hitung ≥ t-tabel, model memenuhi hampir keseluruhan kriteria nilai cut off Goodness of Fit Index yang dipersyaratkan untuk model fit, sehingga dikatakan model fit dengan nilai Construct Reliability (CR) dan Variance Extracted (VE) di atas nilai cut-off, yaitu: CR = 0.846 > 0.7 dan VE = 0.599 > 0.5. Dengan demikian, instrumen Sikap Spiritual pada siswa SD sudah valid dan reliabel.

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