Computing Min and Max Scorings for Two-Sample Ordinal Data.

1996 ◽  
Author(s):  
Jyn R. Whitaker ◽  
Michael D. Whitaker
Keyword(s):  
Ordinal Data

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].

ONE-SIDED NONPARAMETRIC TESTS FOR ORDINAL DATA

2005 ◽  
Vol 101 (6) ◽  
pp. 510 ◽  
Author(s):  
MARKUS NEUHAUSER
Keyword(s):  
Ordinal Data ◽  
Nonparametric Tests

IRTree Model: An Alternative Approach for Self-Reported Ordinal Data Analysis

2019 ◽  
Vol 32 (2) ◽  
pp. 303-323
Author(s):  
Yoonsun Jang ◽  
Meereem Kim ◽  
Juyeon Lee
Keyword(s):  
Data Analysis ◽  
Ordinal Data ◽  
Alternative Approach ◽  
Ordinal Data Analysis

Erratum to the Werts-Linn Comments on Boyle's "Path Analysis and Ordinal Data"

1972 ◽  
Vol 78 (3) ◽  
pp. 689-695
Author(s):  
Charles E. Werts ◽  
Robert L. Linn
Keyword(s):  
Path Analysis ◽  
Ordinal Data

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

scholarly journals Physiotherapists’ reported attitudes to and perceived influences on their continuing professional development: results of an online questionnaire

2020 ◽  
Vol 27 (4) ◽  
pp. 1-13
Author(s):  
Heather Stewart ◽  
Deborah Wisby ◽  
Hazel Roddam
Keyword(s):  
Professional Development ◽  
Ordinal Data ◽  
Continuing Professional Development ◽  
Career Progression ◽  
Organisational Structure ◽  
Online Questionnaire ◽  
Extrinsic Factors ◽  
Factors Influencing ◽  
Negative Effect ◽  
Continued Learning

Background/aims Intrinsic and extrinsic factors influence physiotherapists’ participation in continuing professional development. A number of benefits of and barriers to participation in continuing professional development are identified in the literature, but relatively little is known about factors that influence attitudes towards continued learning. The aim of this study was to identify factors influencing UK physiotherapists’ attitudes towards continuing professional development, with a focus on career point and type of employment, in the context of motivational theories. Methods An online questionnaire consisting of Likert-style questions was used to collect data from UK physiotherapists. Nominal and ordinal data were analysed to determine differences between subgroups within the dataset. Results A total of 205 physiotherapists completed the online questionnaire. Physiotherapists were generally internally motivated towards continuing professional development, but attitudes were influenced by career point and whether physiotherapists worked in the NHS or in the private sector. External factors appeared to have a negative effect on motivation towards continuing professional development. Conclusion: Differences in attitudes at different points on the career path suggest that organisational structure may impede lifelong learning at some stages in career progression, while differences between those working privately and for the NHS may potentially reflect organisational differences between these types of employment.

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