scholarly journals Comparing estimation methods for psychometric networks with ordinal data.

2021 ◽  
Author(s):  
Simran K. Johal ◽  
Mijke Rhemtulla
Keyword(s):  
Ordinal Data ◽  
Estimation Methods

scholarly journals Comparing Estimation Methods for Psychometric Networks with Ordinal Data

2021 ◽  
Author(s):  
Simran Johal ◽  
Mijke Rhemtulla
Keyword(s):  
Sample Size ◽  
Ordinal Data ◽  
Partial Correlation ◽  
False Positive Rate ◽  
Estimation Method ◽  
Network Models ◽  
Estimation Methods ◽  
Edge Weight ◽  
P Value ◽  
Underlying Distribution

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: EBIC graphical lasso with regularized edge estimates (“EBIC”), BIC model selection with partial correlation edge estimates (“BIC”), and multiple regression with p-value-based edge selection and partial correlation edge estimates (“MR”). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias. Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

A Multivariate Approach to Analyze Ordinal Data Collected in Clinical Trials of Analgesic Agents

1979 ◽  
Vol 18 (03) ◽  
pp. 175-179
Author(s):  
E. Mabubini ◽  
M. Rainisio ◽  
V. Mandelli
Keyword(s):  
Clinical Trials ◽  
Multivariate Analysis ◽  
Analgesic Effect ◽  
Ordinal Data ◽  
Ordinal Scale ◽  
Multivariate Approach ◽  
Controlled Clinical Trials ◽  
Second Stage ◽  
Analgesic Agents ◽  
Relevant Factors

After pointing out the drawbacks of the approach commonly used to analyze the data collected in controlled clinical trials carried out to evaluate the analgesic effect of potential agents, the authors suggest a procedure suitable for analyzing data coded according to an ordinal scale. In the first stage a multivariate analysis is carried out on the codec! data and the projection of each result in the space of the most relevant factors is obtained. In the second stage the whole set of these values is processed by distribution-free tests. The procedure has been applied to data previously published by VENTAITBIDDA et al. [18].

Femtocell Selection Scheme for Reducing Unnecessary Handover and Enhancing Downlink QoS in Cognitive Femtocell Networks

2017 ◽  
Author(s):  
Hoang Nhu Dong ◽  
Hoang Nam Nguyen ◽  
Hoang Trong Minh ◽  
Takahiko Saba
Keyword(s):  
Cellular Networks ◽  
Estimation Methods ◽  
Mobility Prediction ◽  
Capacity Estimation ◽  
Femtocell Networks ◽  
Selection Scheme ◽  
Indoor Communications ◽  
Performance Results ◽  
Cognitive Femtocell

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

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