More on Multidimensional Scaling and Unfolding in <i>R</i>: <b>smacof</b> Version 2

Mair, Patrick; Groenen, Patrick J. F.; Leeuw, Jan de

doi:10.18637/jss.v102.i10

Cited by 63 publications

(38 citation statements)

References 55 publications

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“…Finally, we conducted a multidimensional scaling analysis of the raw data of text-based scores, using the smacof package (Mair et al, 2021 ). We specified ordinal data structure and plotted two dimensions.…”

Section: Resultsmentioning

confidence: 99%

“…Achievement, Stimulation and Hedonism values had reliabilities below 0.70 [for a discussion of reliability issues with this measure, refer to Schwartz and Cieciuch ( 2021 )]. To examine the theoretical structure, we conducted a multidimensional scaling analysis with the smacof package (Mair et al, 2021 ) and Euclidean distances based on Pearson's correlations, ordinal data structure, and extracting two dimensions. Compared to the theory-predicted positions (Bilsky et al, 2011 ), our data showed acceptable conceptual similarity (congruence = 0.88) in line with minimum standards for conceptual replication (Fischer and Fontaine, 2011 ; Fischer and Karl, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

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Opportunities and Challenges of Extracting Values in Autobiographical Narratives

et al. 2022

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We report three studies in which we applied a value dictionary to narratives. Our objective was to test a theory-driven value dictionary for extracting valuable information from autobiographical and narrative texts. In Studies 1 (N = 106) and 2 (N = 152), participants wrote short autobiographical narratives and in Study 3 (N = 150), participants wrote narratives based on ambiguous stimuli. Participants in all three studies also completed the Portrait Value Questionnaire as a self-report measure of values. Overall, our results demonstrate that it is possible to extract value-relevant information from these narratives. Extracted values from autobiographical narratives showed average correlations of 0.07 (Study 1) and 0.12 (Study 2) with self-reports compared to an average correlation of 0.01 for the extracted values from implicit motive tasks (Study 3). The correlations with self-reports were in line with previous validation studies. The most salient values in narratives diverged somewhat, with a stronger emphasis on achievement values compared to self-reports, probably due to the nature of salient episodes within one's life that require demonstrating success according to social standards. Benevolence values were consistently most important in both self-ratings and text-based scoring. The value structure emerging from narratives diverged from the theoretically predicted structure, yet broad personally vs. socially focused value dimensions were qualitatively discernible. We highlight opportunities and challenges for future value research using autobiographical stories.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Opportunities and Challenges of Extracting Values in Autobiographical Narratives

et al. 2022

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“…Specifically, Biplot analysis provides a structure of diversity in MDS where data involves variables and not just dissimilarities [31]. In this way, Biplot analysis allows mapping the external variables on MDS configuration, and establishing validation for variables on dimension [32].…”

Section: Multidimensional Scalingmentioning

confidence: 99%

Important factors affecting the transmission rate of COVID-19 in G20 and EU

BOZ¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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Coronavirus 2019 (COVID-19) is an infectious respiratory disease that might be fatal to humans due to severe acute respiratory syndrome coronovirus-2. COVID-19 first appeared in Wuhan, China in 2019 and soon have spread all over the world. Therefore, it was accepted as a global epidemic by the World Health Organization in March 2020. The aim of this study is to reveal the effects of the demographic structure of the countries, their socio-economic development, the precautions, and health practices implemented by the governments against COVID-19 on the rate of transmission until the first peak days (plateaus) are appeared. Due to the socio-economic developments and reaching out the clear and transparent COVID-19 dataset, the sample of the study was formed from G20 and EU countries. The i n terpretable factors affecting the transmission rate of COVID-19 were extracted with factor analysis and multidimensional scaling. Multivariate analysis figures out the effects of th e pr ecautions an d health practices implemented by G20 and EU countries with similar/different socio-economic development characteristics on the transmission rate. For instance, a result obtained from the multivariate analyzes is that COVID-19 cases in developed and developing countries differ from each other at their first plateaus. Another noteworthy inference is that COVID-19 cases are trending similarly within some developed countries with the higher ratio of population (65+) and Human Development Index (HDI). Furthermore, the population ratio (15-64) is itself an explanatory factor that can be used to characterize similar transmission patterns between countries. Consequently, these findings may help state authorities to take urgent precautions and manage such a global epidemic by much more efficient health policies.

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“…The penalized stress function is minimized through numerical optimization using a strategy called SMACOF (Stress Majorization of a Complicated Function) and is implemented in an R package of the same name (de Leeuw & Mair, 2009). For details on multidimensional unfolding and its implementation see Mair et al (2021), Borg and Groenen (2005), and Busing et al (2005).…”

Section: Multidimensional Unfoldingmentioning

confidence: 99%

Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

Nießl

Herrmann

Wiedemann

et al. 2021

WIREs Data Min & Knowl

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In recent years, the need for neutral benchmark studies that focus on the comparison of methods coming from computational sciences has been increasingly recognized by the scientific community. While general advice on the design and analysis of neutral benchmark studies can be found in recent literature, a certain flexibility always exists. This includes the choice of data sets and performance measures, the handling of missing performance values, and the way the performance values are aggregated over the data sets. As a consequence of this flexibility, researchers may be concerned about how their choices affect the results or, in the worst case, may be tempted to engage in questionable research practices (e.g., the selective reporting of results or the post hoc modification of design or analysis components) to fit their expectations. To raise awareness for this issue, we use an example benchmark study to illustrate how variable benchmark results can be when all possible combinations of a range of design and analysis options are considered. We then demonstrate how the impact of each choice on the results can be assessed using multidimensional unfolding. In conclusion, based on previous literature and on our illustrative example, we claim that the multiplicity of design and analysis options combined with questionable research practices lead to biased interpretations of benchmark results and to over-optimistic conclusions. This issue should be considered by computational researchers when designing and analyzing their benchmark studies and by the scientific community in general in an effort towards more reliable benchmark results.

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More on Multidimensional Scaling and Unfolding in R: smacof Version 2

Cited by 63 publications

References 55 publications

Opportunities and Challenges of Extracting Values in Autobiographical Narratives

Opportunities and Challenges of Extracting Values in Autobiographical Narratives

Important factors affecting the transmission rate of COVID-19 in G20 and EU

Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

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